Hubbry Logo
Hard and soft scienceHard and soft scienceMain
Open search
Hard and soft science
Community hub
Hard and soft science
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Hard and soft science
Hard and soft science
Hard and soft science
View on Wikipedia
from Wikipedia

Hard science and soft science are colloquial terms used to compare scientific fields on the basis of perceived methodological rigor, exactitude, and objectivity.[1][2][3] In general, the formal sciences and natural sciences are considered hard science by their practitioners, whereas the social sciences and other sciences are described by them as soft science.[4]

Precise definitions vary,[5] but features often cited as characteristic of hard science include producing testable predictions, performing controlled experiments, relying on quantifiable data and mathematical models, a high degree of accuracy and objectivity, higher levels of consensus, faster progression of the field, greater explanatory success, cumulativeness, replicability, and generally applying a purer form of the scientific method.[2][6][7][8][9][10][11][12][excessive citations] A closely related idea (originating in the nineteenth century with Auguste Comte) is that scientific disciplines can be arranged into a hierarchy of hard to soft on the basis of factors such as rigor, "development", and whether they are basic or applied.[5][13]

Philosophers and historians of science have questioned the relationship between these characteristics and perceived hardness or softness. The more "developed" hard sciences do not necessarily have a greater degree of consensus or selectivity in accepting new results.[6] Commonly cited methodological differences are also not a reliable indicator. For example, social sciences such as psychology and sociology use mathematical models extensively, but are usually considered soft sciences.[1][2] While scientific controls are cited as a methodological difference between hard and soft sciences,[14] in certain natural sciences, like astronomy and geology, it is impossible to perform controlled experiments to test most hypotheses and observation and natural experiments are primarily used instead.[15][16] Survey data about the replication crisis among researchers strongly suggests that the failure to reproduce published findings has impacted the natural and applied sciences along with psychology and the social sciences.[17][18] However, there are some observable differences between hard and soft sciences. For example, hard sciences make more extensive use of graphs,[5][19] and soft sciences are more prone to a rapid turnover of buzzwords.[20]

The metaphor has been criticised for unduly stigmatizing soft sciences, creating an unwarranted imbalance in the public perception, funding, and recognition of different fields.[2][3][21]

History of the terms

[edit]

The origin of the terms "hard science" and "soft science" is obscure. The earliest attested use of "hard science" is found in an 1858 issue of the Journal of the Society of Arts,[22][23] but the idea of a hierarchy of the sciences can be found earlier, in the work of the French philosopher Auguste Comte (1798‒1857). He identified astronomy as the most general science,[note 1] followed by physics, chemistry, biology, then sociology. This view was highly influential, and was intended to classify fields based on their degree of intellectual development and the complexity of their subject matter.[6]

The modern distinction between hard and soft science is often attributed to a 1964 article published in Science by John R. Platt. He explored why he considered some scientific fields to be more productive than others, though he did not actually use the terms themselves.[24][25] In 1967, sociologist of science Norman W. Storer specifically distinguished between the natural sciences as hard and the social sciences as soft. He defined hardness in terms of the degree to which a field uses mathematics and described a trend of scientific fields increasing in hardness over time, identifying features of increased hardness as including better integration and organization of knowledge, an improved ability to detect errors, and an increase in the difficulty of learning the subject.[6][26]

Empirical support

[edit]

In the 1970s sociologist Stephen Cole conducted a number of empirical studies attempting to find evidence for a hierarchy of scientific disciplines, and was unable to find significant differences in terms of core of knowledge, degree of codification, or research material. Differences that he did find evidence for included a tendency for textbooks in soft sciences to rely on more recent work, while the material in textbooks from the hard sciences was more consistent over time.[6] After he published in 1983, it has been suggested that Cole might have missed some relationships in the data because he studied individual measurements, without accounting for the way multiple measurements could trend in the same direction, and because not all the criteria that could indicate a discipline's scientific status were analysed.[27]

In 1984, Cleveland performed a survey of 57 journals and found that natural science journals used many more graphs than journals in mathematics or social science, and that social science journals often presented large amounts of observational data in the absence of graphs. The amount of page area used for graphs ranged from 0% to 31%, and the variation was primarily due to the number of graphs included rather than their sizes.[28] Further analyses by Smith in 2000,[5] based on samples of graphs from journals in seven major scientific disciplines, found that the amount of graph usage correlated "almost perfectly" with hardness (r=0.97). They also suggested that the hierarchy applies to individual fields, and demonstrated the same result using ten subfields of psychology (r=0.93).[5]

In a 2010 article, Fanelli proposed that we expect more positive outcomes in "softer" sciences because there are fewer constraints on researcher bias. They found that among research papers that tested a hypothesis, the frequency of positive results was predicted by the perceived hardness of the field. For example, the social sciences as a whole had a 2.3-fold increased odds of positive results compared to the physical sciences, with the biological sciences in between. They added that this supported the idea that the social sciences and natural sciences differ only in degree, as long as the social sciences follow the scientific approach.[7]

In 2013, Fanelli tested whether the ability of researchers in a field to "achieve consensus and accumulate knowledge" increases with the hardness of the science, and sampled 29,000 papers from 12 disciplines using measurements that indicate the degree of scholarly consensus. Out of the three possibilities (hierarchy, hard/soft distinction, or no ordering), the results supported a hierarchy, with physical sciences performing the best followed by biological sciences and then social sciences. The results also held within disciplines, as well as when mathematics and the humanities were included.[29]

The perception of hard vs soft science is influenced by gender bias with a higher proportion of women in a given field leading to a "soft" perception even within STEM fields. This perception of softness is accompanied by a devaluation of the field's worth.[30]

Criticism

[edit]

Critics of the concept argue that soft sciences are implicitly considered to be less "legitimate" scientific fields,[2] or simply not scientific at all.[31] An editorial in Nature stated that social science findings are more likely to intersect with everyday experience and may be dismissed as "obvious or insignificant" as a result.[21] Being labelled a soft science can affect the perceived value of a discipline to society and the amount of funding available to it.[3] In the 1980s, mathematician Serge Lang successfully blocked influential political scientist Samuel P. Huntington's admission to the US National Academy of Sciences, describing Huntington's use of mathematics to quantify the relationship between factors such as "social frustration" (Lang asked Huntington if he possessed a "social-frustration meter") as "pseudoscience".[11][32][33] During the late 2000s recessions, social science was disproportionately targeted for funding cuts compared to mathematics and natural science.[34][35] Proposals were made for the United States' National Science Foundation to cease funding disciplines such as political science altogether.[21][36] Both of these incidents prompted critical discussion of the distinction between hard and soft sciences.[11][21]

See also

[edit]

Notes

[edit]

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Hard and soft science

View on Grokipedia
from Grokipedia
Hard and soft sciences refer to a colloquial distinction among scientific disciplines based on perceived levels of methodological rigor, precision, quantification, and predictive capability, with hard sciences—such as , chemistry, and —employing controlled experiments, mathematical modeling, and replicable outcomes to achieve high degrees of empirical certainty, whereas soft sciences—including , , and —typically involve more qualitative, interpretive approaches to studying complex, variable human behaviors and social systems that resist precise prediction. This classification highlights differences in the solidity of accumulation, where hard sciences facilitate stronger consensus through falsifiable hypotheses and standardized metrics. The terms gained prominence in the United States following , originating in 1945 with engineer Gano Dunn's assertion that "prediction is the test of a 'hard' ," amid debates over federal funding allocation by institutions like the , which prioritized disciplines demonstrating technological applicability and intellectual integration. By the , scholars such as Norman Storer formalized the array through analyses of citation patterns and mathematical tool usage, positioning hard sciences as models of scientific progress while critiquing soft sciences for weaker consensus formation. Politically, the distinction influenced resource distribution, with soft sciences facing scrutiny for perceived inefficacy in addressing social issues, as seen in congressional awards targeting questionable expenditures and proposals to curtail their support. Empirical assessments underscore the distinction's validity, revealing that in social and behavioral sciences exhibits lower cumulativeness and replicability than in physical sciences, with meta-analyses showing divergent study agreements and persistent challenges in reproducing key findings, particularly amid the predominantly affecting softer fields. Controversies persist, including proposals to invert the labels by arguing that soft sciences confront greater inherent complexities, yet such views conflict with of methodological limitations and lower statistical power in replications from behavioral domains. Despite critiques of hierarchical implications, the framework remains relevant for evaluating scientific reliability and informing policy on investment.

Definitions and Core Distinctions

Defining Hard Sciences

Hard sciences, also termed or physical sciences, comprise disciplines that systematically investigate the material universe using empirical methods to formulate predictive laws grounded in quantifiable data and repeatable experiments. These fields prioritize , wherein hypotheses are rigorously tested against observable evidence, often yielding high levels of inter-researcher agreement and cumulativeness of knowledge due to standardized protocols and mathematical rigor. Core examples include physics, which elucidates fundamental forces through equations like F = ma (Newton's second law, formulated in 1687); chemistry, focusing on atomic interactions via and reaction kinetics; astronomy, mapping with tools like Kepler's laws (derived empirically in 1609–1619); and , particularly at molecular levels, employing techniques such as to quantify genetic mechanisms. Unlike interpretive approaches, hard sciences demand control over variables—e.g., isolating reactants in a lab for precise measurements—and leverage to minimize error, achieving replication rates often exceeding 90% in foundational experiments. This methodological stringency enables causal inference, as seen in validations at CERN's , confirming the in 2012 with 5-sigma certainty.

Defining Soft Sciences

Soft sciences denote academic disciplines, primarily within the social sciences, that systematically study , social structures, cultural dynamics, and interpretive phenomena using predominantly observational, qualitative, and statistical approaches rather than controlled experimentation. These fields emerged as efforts to apply scientific methods to inherently variable and context-dependent subjects, where relies on probabilistic models and longitudinal data rather than deterministic laws. Key examples include , , , , and , each grappling with multifaceted variables like individual , group interactions, and institutional incentives that resist isolation in settings. Psychology exemplifies these challenges by investigating complex human behavior amid numerous confounding variables and individual differences, utilizing experiments, statistics, and neuroimaging techniques while contending with ethical barriers to stringent controls, susceptibility to bias, and a reliance on interpretive probabilities over universal laws, resulting in relative imprecision compared to hard sciences. A defining feature of soft sciences is the challenge in achieving high levels of empirical cumulativeness, defined as the consistency and convergence of findings across independent studies, due to factors such as small effect sizes, subjectivity, and the non-stationarity of systems influenced by evolving norms and technologies. For instance, replicability rates in —a core soft science—have been documented at around 36-50% in large-scale replication projects, contrasting sharply with near-universal success in physics experiments under similar protocols. This stems from methodological necessities like reliance on self-reported data, ethical prohibitions on manipulative interventions, and the interpretive latitude in qualitative analysis, which introduce variance not easily mitigated by . The distinction underscores a of objectivity, with soft sciences often critiqued for greater susceptibility to researcher bias and paradigmatic shifts, as evidenced by historical upheavals like the in post-2010, where initial priming effects failed to hold in preregistered validations. Nonetheless, advancements such as analytics and techniques from have incrementally enhanced rigor, though core limitations persist owing to the of sentient agents. Proponents argue this does not invalidate the scientific status but reflects adaptive methodologies suited to their domains, prioritizing explanatory depth over predictive precision.

Overlaps and Borderline Fields

Economics frequently occupies a borderline position, incorporating mathematical and statistical tools characteristic of hard sciences while analyzing subject to the complexities and variability of soft sciences. Economists often employ econometric models and , akin to physical modeling, yet the discipline struggles with replicability and precise prediction due to unobservable variables and agent heterogeneity. Thomas Mayer argued in 1980 that aspirations to elevate to hard science status via mathematical rigor are largely wishful, as the subject's reliance on assumptions and inability to fully control social experiments undermines such goals. Cognitive science exemplifies overlaps by integrating computational modeling and —rooted in hard science methodologies—with psychological and linguistic inquiries that emphasize subjective interpretation. This field uses algorithms and brain imaging for formal analysis of , yet incorporates qualitative data from human subjects, leading to hybrid rigor where predictive simulations meet interpretive challenges in areas like . Neuroscience bridges the divide, primarily classified as hard due to its biological foundations in and , but bordering soft sciences through cognitive applications involving behavioral data and ethical constraints on experimentation. Techniques like fMRI provide quantifiable neural correlates, yet interpretations of or introduce subjectivity akin to . This interdisciplinary nature fosters advances, such as in , where empirical neural mapping informs behavioral theories, though replicability varies by subfield.

Historical Development

Early Conceptual Foundations

![Socrates][float-right] The conceptual foundations of distinguishing between what would later be termed hard and soft sciences trace back to ancient Greek philosophy, particularly Aristotle's classification of knowledge in works such as the Nicomachean Ethics and Metaphysics. Aristotle divided intellectual pursuits into three categories: theoretical sciences, aimed at contemplating unchanging truths about the natural world and divine principles (encompassing physics, mathematics, and theology); practical sciences, focused on human action and ethical conduct (including ethics and politics); and productive sciences, concerned with creation (such as arts and crafts). Theoretical sciences, dealing with necessary causes and universal principles, were deemed highest and most exact, laying early groundwork for viewing inquiries into inanimate nature as more rigorous and less contingent than those into human behavior. In the , Auguste Comte's positivist formalized a hierarchical progression of sciences in his Cours de philosophie positive (1830–1842), ordering them by increasing complexity and decreasing generality: , astronomy, physics, chemistry, , and culminating in . Natural sciences, foundational and more amenable to precise laws due to simpler phenomena, supported the emergence of social sciences, which Comte viewed as positive but inherently more complex, involving human volition and thus less predictable. This framework underscored methodological differences, with earlier sciences relying on and deduction, while social sciences required integration of prior knowledge amid greater variability. Philosophers like and further refined these ideas. In A System of Logic (1843), Mill differentiated sciences of physical nature, amenable to geometric-style deduction from general laws, from those of mind and society, which necessitate "inverse deductive" or historical methods to account for individual agency and , rendering the latter less exact. Dilthey, in his Introduction to the Human Sciences (1883), proposed a methodological divide: natural sciences erklären (explain via causal laws), while human sciences (understand through empathetic interpretation of ), rejecting metaphysical dualism but highlighting irreducible differences in objects of study—impersonal mechanisms versus meaningful human expressions. These distinctions emphasized that inquiries into human affairs inherently confront contingency and subjectivity, contrasting with the relative determinacy of natural phenomena.

Mid-20th Century Formalization

The explicit distinction between hard and soft sciences emerged in the immediate post-World War II period, amid growing institutional differentiation and funding competitions between natural and social sciences . In 1945, engineer Gano Dunn coined the terms in a speech to the , characterizing hard sciences such as physics by their capacity for precise, verifiable predictions, in contrast to soft sciences like , which he viewed as yielding more tentative and probabilistic outcomes due to inherent complexities in . This usage reflected broader anxieties over scientific authority and resource allocation, particularly as federal agencies like the (NSF), established in 1950, prioritized natural sciences for their alignment with military and technological imperatives during the . By the and early , the terminology permeated sociological analyses of , often tied to perceptual studies and institutional metrics. Charles Osgood's technique, developed in the and detailed in his 1957 book The Measurement of Meaning, employed bipolar scales to quantify connotative evaluations of concepts, including scientific disciplines; scales implicitly captured dimensions of perceived rigor and objectivity that paralleled hard-soft contrasts, such as "exact-inexact" or "predictable-unpredictable." These tools facilitated empirical assessments of how disciplines were rated on scales of hardness, influencing debates in the of . Formal sociological formalization accelerated in the mid-1960s, with Norman Storer's 1966 paper in the Bulletin of the Medical Library Association providing a systematic framework. Storer defined by criteria including high consensus on paradigms, extensive quantification, rapid cumulation of , and robust institutional norms for replication and , attributing greater to fields like physics over due to their methodological controls and lower variability in findings. In a follow-up article, Storer extended this to observable markers, such as the prevalence of mathematical formalism and impersonality in authorship (e.g., use of initials over full names in citations), which he empirically linked to harder sciences' emphasis on objective over personal interpretation. Quantitative formalization followed soon after, as bibliometric methods sought to operationalize the distinction. In 1969, applied in Little Science, Big Science to derive "" indices based on publication growth and obsolescence rates; physics exhibited scores of 60-70 (indicating rapid, cumulative progress), while scored 40-45, reflecting slower integration and higher dispute rates. These metrics, grounded in observable like journal citation patterns, provided a causal basis for viewing as tied to and empirical convergence rather than mere subject matter. This era's developments were not merely descriptive but politically charged, coinciding with NSF budget expansions for social sciences (rising over 700% from 1960 to 1969) that provoked critiques of softness as inefficiency; proponents of the distinction argued it justified differential funding by highlighting hard sciences' superior predictive track records in applications like . Critics, including physicist Lawrence Cranberg in 1965, countered that social phenomena's complexity rendered them arguably "harder" to model, challenging the implications without undermining the core methodological divide. Overall, mid-century formalization shifted the from informal contrasts to testable frameworks, emphasizing replicability and quantification as hallmarks of .

Post-1970s Evolution and Usage

In the 1970s and 1980s, the hard/soft science distinction increasingly informed funding allocations and political rhetoric, as social sciences expanded amid skepticism over their rigor compared to natural sciences. The National Science Foundation's social science budget grew substantially during this period, yet faced criticism for supporting projects deemed ideologically driven or methodologically lax, exemplified by Senator William Proxmire's Golden Fleece Awards, which from 1975 targeted NSF-funded studies in areas like animal behavior interpreted through human analogies, portraying soft sciences as frivolous expenditures of taxpayer funds. Sociologist Stephen Cole's 1983 analysis of citation patterns and consensus rates challenged the hierarchy by arguing that at disciplinary frontiers, soft fields like sociology exhibited comparable rates of progress to hard sciences, though overall cumulativeness remained lower in social sciences with Price's Index scores of 40-45 versus 60-70 for physics. The 1981 Reagan administration budget proposals explicitly aimed to curtail funding for "soft" social sciences while preserving allocations for hard sciences like physics and , reflecting a view that the former offered less reliable returns on investment due to interpretive variability. , in a 1981 interview, dismissed social sciences as failing basic scientific criteria, stating they lacked predictive and treated theories as unfalsifiable "cargo cult" rituals rather than empirical tests. These debates embedded the terminology in institutional discourse, with hard sciences positioned as exemplars of objectivity amid rising interdisciplinary efforts that nonetheless reinforced perceptual divides. The 1990s "" amplified usage of the distinction, pitting defenders of hard sciences' empirical objectivity against postmodern approaches in science and technology studies (STS), a soft field that questioned universal truths in favor of social construction. The 1996 crystallized this, as physicist submitted a paper laden with fabricated claims and postmodern jargon to the journal , exposing what he and critics like Jean Bricmont argued was intellectual undermining hard sciences' causal foundations; their 1997 book extended this critique to soft ' encroachment on scientific authority. Into the 21st century, empirical evidence from the crisis has substantiated methodological disparities, with soft fields like showing stark replication failures—a multicenter study replicated only 36% of 100 high-profile experiments, attributing issues to flexible analyses and absent in hard s' controlled settings. Hard sciences, by contrast, exhibit higher reproducibility rates, as in physics where meta-analyses confirm consistency within bounds across studies. usage persists, as in the 2012 U.S. House vote to eliminate NSF funding for unless tied to , prioritizing hard sciences' predictive utility over soft fields' contested validity. Despite calls to retire the terms for blurring lines in fields like , the distinction endures in assessments of epistemic reliability, with soft sciences often adapting hard methods—such as preregistration—to mitigate crises.

Methodological Differences

Experimental Control and Quantification in Hard Sciences

In hard sciences such as physics, chemistry, and , experimental control entails designing tests where a single independent variable is deliberately manipulated while all other potential influences—known as variables—are held constant or randomized to prevent . This approach, exemplified by the use of control groups that mirror experimental conditions except for the variable under study, enables researchers to attribute observed outcomes directly to the manipulated factor rather than extraneous effects. For instance, in experiments, temperature, concentration, and catalysts are precisely regulated to isolate reaction rates, as uncontrolled variations could mask true mechanisms. Quantification in these fields relies on objective, numerical measurement of variables using calibrated instruments and standardized units, facilitating replicable results and . In physics, phenomena are quantified through metrics like in newtons or in joules, with experiments incorporating error propagation to express precision—such as the measurement of Planck's constant yielding values around 6.626 × 10^{-34} J·s with uncertainties below 0.01% in modern determinations. Chemistry employs techniques like to quantify atomic concentrations to parts per million, while uses methods such as for cell counts accurate to single digits. These practices support , including p-values and confidence intervals, to evaluate effect sizes and rule out chance. The integration of control and quantification enhances , a hallmark of hard sciences, where protocols are detailed enough for independent labs to yield consistent findings under identical conditions. For example, determinations of fundamental constants like the have converged to 299,792,458 m/s across global efforts since the , reflecting rigorous controls against environmental noise and systematic errors. This contrasts with less controlled domains, underscoring how such methods minimize variability and build cumulative knowledge.

Observational and Interpretive Methods in Soft Sciences

Observational methods in soft sciences, such as and , primarily involve systematic recording of behaviors in natural or semi-natural settings without direct manipulation of variables, as controlled experimentation is often infeasible due to ethical constraints or the complexity of human subjects. These include , where researchers monitor phenomena like social interactions in everyday environments, and , in which the researcher immerses in the group being studied to gain insider perspectives. For instance, in , structured observation employs predefined coding schemes to quantify events like institutional interactions, aiming for reliability through inter-observer agreement metrics. Interpretive methods complement observation by emphasizing the subjective meanings and cultural contexts underlying behaviors, drawing on qualitative techniques like , , and . In , Clifford Geertz's concept of "" exemplifies this approach, advocating detailed interpretation of actions—such as rituals—to uncover layered cultural significances rather than surface-level events. Similarly, in , interpretive analyses explore non-monetary meanings of prices, integrating to examine how market attribute social value beyond incentives. These methods prioritize , or empathetic understanding, often through in-depth interviews and textual , to construct narratives of . Despite their utility in capturing real-world nuance, these methods face inherent challenges that limit compared to experimental designs. , where researchers' preconceptions influence data interpretation, and the —altered behaviors due to awareness of being observed—undermine objectivity. Replicability suffers from contextual variability; for example, participant observations in yield high but resist standardization across studies. Ethical dilemmas arise in covert observations, violating , while overt methods may disrupt natural behaviors. Proponents argue these approaches are essential for complex systems like societies, yet critics highlight their reliance on researcher subjectivity, often yielding non-falsifiable interpretations that prioritize narrative over empirical rigor.

Empirical Evidence Supporting the Distinction

Replicability and Cumulativeness Metrics

Empirical assessments of replicability, defined as the ability to obtain consistent results from independent replications under similar conditions, reveal stark differences between hard and soft sciences. In psychology, a large-scale effort by the Open Science Collaboration in 2015 attempted to replicate 100 experiments published in top journals, achieving statistical significance in only 39% of cases, compared to 97% in the originals, with replicated effect sizes averaging about half the original magnitude. Similar low rates persist in social sciences, where a 2018 project replicating 21 high-impact studies in Nature and Science yielded successful replications in just 62% of cases, often with diminished effects. In contrast, fields like physics and chemistry exhibit replication success rates exceeding 85-90%, as evidenced by self-reported surveys and targeted reanalyses, attributable to standardized protocols, precise instrumentation, and lower susceptibility to researcher degrees of freedom. These disparities underscore methodological rigor in hard sciences, where controlled environments minimize variability, versus the interpretive flexibility and small-sample issues prevalent in soft sciences. Cumulativeness, gauged by the convergence of findings across studies—such as reduced heterogeneity in meta-analytic effect sizes—further differentiates the fields. Larry Hedges' 1987 analysis of meta-analyses across disciplines found that physical sciences demonstrate greater cumulativeness, with between-study variance in effect sizes comprising a smaller proportion of total variance (often under 20%), indicating consistent buildup of knowledge through refined estimates. In social and behavioral sciences, however, between-study variance dominates (frequently 50% or more), reflecting persistent discrepancies and slower knowledge accumulation due to confounding variables like cultural shifts or subjective measurement. This metric aligns with broader patterns: natural sciences accrue paradigms with incremental validation, as in ' foundational replications since the 1920s, while soft sciences show higher rates of contradictory meta-analytic outcomes, exemplified by oscillating estimates in economic models of behavior over decades. Even acknowledging non-absolute cumulativeness in hard sciences—due to paradigm shifts like relativity supplanting Newtonian —the relative stability supports the distinction, as soft fields rarely achieve such hierarchical integration. These metrics, derived from quantitative syntheses rather than anecdotal claims, provide objective evidence that hard sciences foster more reliable epistemic progress.

Predictive Power and Falsifiability Outcomes

Theories in hard sciences frequently demonstrate superior through precise, quantitative forecasts that align closely with empirical observations, enabling clear falsification when discrepancies arise. For example, the in predicted the existence of in 1928, which was experimentally confirmed with the discovery of the in 1932 by Carl Anderson, illustrating how theoretical predictions in physics can be tested and verified with high specificity. Similarly, general relativity's prediction of the bending of starlight by the Sun's gravity was observationally confirmed during the 1919 solar eclipse expedition led by , providing decisive evidence that falsified Newtonian alternatives under extreme conditions. These outcomes reflect the capacity of hard science frameworks to generate testable hypotheses with narrow confidence intervals, often achieving predictive accuracies exceeding 99.999% in domains like . In contrast, soft sciences exhibit diminished predictive power and , as theories often rely on correlational data with multiple confounding variables, leading to vague or post-hoc interpretations that resist decisive refutation. Paul Meehl argued that in "soft" areas of , theoretical constructs lack the riskiness required for meaningful falsification, with predictions typically framed in ways that allow auxiliary adjustments rather than outright rejection, resulting in theories that persist without corroboration or disproof. Empirical assessments underscore this: a 2015 replication effort by the Open Science Collaboration tested 100 experiments and found only 36% produced significant results in the same direction as originals, indicating low reliability of predictions across contexts. In , a field often grouped with soft sciences, macroeconomic models have historically failed to forecast recessions accurately, with the International Monetary Fund's models underperforming simple benchmarks like random walks in out-of-sample predictions during the . Falsifiability outcomes further delineate the distinction, as hard sciences resolve theoretical disputes through repeatable experiments that eliminate ambiguity, whereas soft sciences frequently encounter the Duhem-Quine problem amplified by human behavioral complexity, where failed predictions are attributed to unmodeled factors rather than core theory flaws. In physics, falsification has been instrumental, such as the Michelson-Morley experiment in 1887 disproving the luminiferous hypothesis, paving the way for relativity. Soft science parallels show protracted debates; for instance, Freudian has evaded falsification despite empirical challenges, as proponents invoke interpretive flexibility, contrasting with the self-correcting trajectory in natural sciences where anomalous data prompts shifts. Meta-analyses of effect sizes in reveal median replicability rates below 50%, correlating with weaker falsifiability due to low statistical power and p-hacking incentives, whereas hard science benchmarks, like those in chemistry, maintain near-universal replication for foundational laws. This disparity in outcomes supports the empirical validity of the hard-soft divide, with hard sciences accumulating robust, predictive at rates unattainable in softer domains amid systemic methodological hurdles.

Criticisms of the Distinction

Arguments for Equivalence or Obsolescence

Some scholars contend that the hard-soft science distinction reflects differences in subject complexity and experimental feasibility rather than fundamental methodological disparities, rendering the categories equivalent in their adherence to empirical standards. For instance, both domains rely on the , where hypotheses are tested against data, with variations attributable to the inherent uncontrollability of social phenomena compared to physical ones, not to inferior rigor in soft fields. This view posits that apparent gaps in precision arise from the greater number of confounding variables in , which demand sophisticated statistical controls rather than bespoke instrumentation, as seen in econometric modeling that mirrors physical simulations in predictive accuracy when data volume increases. Advancements in computational tools and have further eroded perceived divides, enabling soft sciences to achieve replicable, quantitative outcomes akin to hard sciences; examples include applications in for in behavioral datasets, yielding falsifiable predictions with error rates comparable to geophysical modeling. Critics of the distinction, such as atmospheric scientist Marshall Shepherd, argue it perpetuates an outdated that ignores shared challenges like interpretive ambiguity in or climate projections, where probabilistic forecasts in hard fields parallel those in . Similarly, Jared highlights that soft sciences often demand "stronger inference" from noisier data, making them intellectually more demanding, thus questioning any presumption of soft fields' lesser status. The obsolescence of the binary is attributed to its origins in mid-20th-century disciplinary turf wars, where natural scientists invoked "hardness" to assert superiority amid funding competitions, a framing now seen as politically motivated rather than epistemically grounded. Interdisciplinary fields like and exemplify this blurring, integrating controlled experiments with observational data to produce cumulative knowledge indistinguishable in structure from traditional hard paradigms. Proponents maintain that retaining the labels hinders collaboration, as evidenced by stalled progress in areas like policy until soft insights from were quantified and integrated with hard virology data during the response, achieving equivalence in through hybrid methodologies.

Claims of Gender or Cultural Bias

Some scholars in science and technology studies (STS) and have argued that the hard/soft science distinction perpetuates gender stereotypes by associating "hard" sciences with masculine traits such as objectivity, rigor, and quantification, while deeming "soft" sciences as feminine, subjective, and less authoritative. This perspective posits that the dichotomy reinforces patriarchal structures in academia, where hard sciences receive greater prestige, funding, and resources, marginalizing fields like or that attract more women. For instance, Evelyn Fox Keller's analysis links the perceived masculinity of scientific knowledge to the dominance of "hard" physical sciences, suggesting that gendered cultural norms shape epistemological hierarchies rather than inherent methodological differences. Empirical studies have explored whether representation influences labeling practices. A 2022 experiment published in the Journal of Experimental found that participants were more likely to classify a hypothetical STEM discipline as a "soft " when informed of higher female participation rates (e.g., 70% women versus 30%), even when methodological descriptions were identical across conditions. This effect persisted among self-identified scientists, indicating implicit in perceptions of scientific legitimacy tied to gender demographics. Proponents of this view, including in her 1986 book The Science Question in , contend that such biases undermine the validity of the distinction, advocating for a reevaluation of objectivity as a culturally constructed ideal rather than a neutral standard. Claims of cultural bias in the classification are less prevalent but similarly frame the hard/soft divide as ethnocentric, privileging Western, quantitative paradigms over indigenous or non-Western knowledge systems that emphasize relational, qualitative approaches. Critics argue this Western bias devalues disciplines incorporating cultural contexts, such as , by labeling them "soft" and excluding diverse epistemologies from core scientific status. However, these assertions often originate from interdisciplinary fields like STS, which exhibit systemic ideological skews toward , potentially overstating bias at the expense of methodological disparities in replicability and predictive accuracy observed across global datasets.

Defenses from First-Principles Reasoning

Causal Realism and Epistemic Rigor

Causal realism maintains that causation constitutes an objective, mind-independent relation in the world, whereby certain events or processes produce specific effects through underlying mechanisms. This perspective underpins defenses of the hard-soft science distinction by emphasizing that genuine scientific understanding requires elucidating these mechanisms rather than settling for descriptive regularities or probabilistic associations. In practice, epistemic rigor demands methodological tools capable of isolating causal influences, such as randomized controlled trials or parametric interventions, which permit counterfactual reasoning about what would occur under altered conditions. Hard sciences operationalize this through experimental designs that manipulate variables in controlled environments, enabling direct tests of causal hypotheses and alignment with the structural causal models advocated by Pearl, where interventions reveal invariant mechanisms. For instance, in physics, Newton's laws derive from repeatable manipulations demonstrating as a causal producer of , yielding predictive laws grounded in fundamental interactions. Such approaches ascend Pearl's "ladder of causation" to higher levels, incorporating interventions and counterfactuals, which foster cumulative knowledge and internal consensus characteristic of these fields. In contrast, soft sciences often operate at the associational level due to ethical constraints on experimentation and the inherent complexity of human systems, where confounders proliferate and mechanisms remain opaque. Epistemic rigor, from a first-principles standpoint, insists on transparency about these limitations: claims of causality from observational studies must invoke strong assumptions about unobservables, which, if untested, undermine reliability. Defenders contend that equating the two overlooks how hard sciences' adherence to causal identification—via quantifiable, falsifiable mechanisms—better approximates objective truth, while soft sciences' interpretive flexibility risks conflating artifactual patterns with real causation, as evidenced by lower replicability rates in domains like psychology. This distinction preserves scientific progress by reserving stronger epistemic warrant for methods that probe reality's causal fabric directly.

Role of Complexity Versus Methodological Laxity

Proponents of the hard-soft science distinction argue that inherent complexity in subject matter—such as the multiplicity of interacting variables in versus the relative invariance of physical laws—contributes to differences in epistemic outcomes, but emphasize that methodological laxity in amplifies these challenges far beyond what complexity alone necessitates. In fields like , complex phenomena such as or chaotic dynamics are modeled with mathematical precision and subjected to stringent experimental controls, yielding high replicability rates approaching 100% in core results, despite non-linearities. By contrast, often encounter lower replication success, with psychological studies replicating at only 36-50% in large-scale efforts, attributable not solely to contextual variability but to practices like underpowered samples and flexible analytic choices that inflate false positives. Evidence from meta-analyses reveals systemic methodological leniency in softer disciplines, where the proportion of published positive results reaches 91.5% in and , compared to 70.2% in space sciences, with of reporting positives 2.3 to 5 times higher in social and behavioral fields due to experimenter , favoring significance, and reduced statistical power. This pattern persists even after controlling for discipline, implicating interpretive flexibility over raw complexity, as hard sciences enforce scrutiny and mathematical falsification absent in many social inquiries. Causal identification, central to realist accounts of scientific , fares better in hard sciences through manipulable interventions that isolate mechanisms, whereas soft sciences frequently rely on observational correlations prone to , exacerbating irreproducibility when rigor is relaxed. Recent interventions underscore the primacy of method over inherent complexity: preregistration of hypotheses, transparent data practices, and larger samples have elevated psychological replication rates to nearly 90% in targeted replications, demonstrating that historical laxity—often rationalized by appeals to human unpredictability—rather than insurmountable barriers, drives disparities. In essence, while soft sciences grapple with emergent, agentic systems resistant to full reduction, defenses maintain that adopting hard-science protocols like precise quantification and preemptive error correction would yield cumulative knowledge gains, countering narratives that equate complexity with inevitable imprecision. This view aligns with first-principles insistence on verifiable mechanisms over interpretive latitude, preserving the distinction as a call for elevated standards rather than dismissal.

Ideological Biases and Objectivity Challenges

Evidence of Political Influence in Soft Sciences

Surveys of faculty political affiliations reveal a pronounced left-liberal dominance in departments. In highly ranked U.S. national universities, the Democrat-to-Republican ratio among faculty stands at 11.5:1. Across broader surveys of and fields, the average liberal-to-conservative ratio approximates 15:1. This asymmetry, which has intensified from roughly 4:1 in the to 13:1 by the , reflects not only self-selection but also institutional hiring and retention patterns favoring progressive viewpoints. Such ideological homogeneity fosters and suppression of dissenting research, particularly on topics challenging progressive orthodoxies like innate sex differences or group disparities in cognitive abilities. A 2024 study of U.S. professors found higher rates of among those more confident in conclusions, such as biological influences on gender roles, potentially skewing perceived toward left-leaning interpretations. This dynamic aligns with models positing that in manifests through theories that flatter liberals (e.g., portraying them as more rational or empathetic) while disparaging conservatives, influencing selection, data interpretation, and publication decisions. Institutional outputs exhibit parallel distortions. An analysis of press releases since 2000 identified pervasive left-wing , with disproportionate emphasis on narratives aligning with progressive priorities, such as equity over merit or systemic frameworks, while downplaying or omitting counterevidence. In and replication efforts, highly politicized findings—irrespective of slant—show reduced replicability, though the prevalence of liberal-biased claims amplifies this issue in fields like . These patterns, documented across multiple disciplines including and , indicate that systemic left-leaning in academia undermines the objectivity of soft sciences by prioritizing ideological over empirical .

Replication Crisis as a Symptom

The refers to the widespread failure to reproduce findings from prior studies, particularly in fields such as and other social sciences, where large-scale replication attempts have yielded success rates far below expectations. In a landmark project by the Collaboration, researchers attempted to replicate 100 experiments from top journals published in 2008; only 36% produced statistically significant results matching the original direction, compared to 97% of the originals, with replicated effect sizes averaging about half the original magnitude. Similar efforts in social-behavioral sciences have reported average replication rates around 50%, highlighting systemic issues like low statistical power, favoring positive results, and questionable research practices such as p-hacking and selective reporting. These problems are less prevalent in hard sciences like physics and chemistry, where experimental controls are more standardized and effect sizes often larger, resulting in higher rates despite isolated challenges in areas like preclinical . In soft sciences, manifests as a symptom of deeper methodological vulnerabilities compounded by ideological influences, where predominant left-leaning political orientations among researchers—estimated at over 80% in —foster environments prone to and resistance to falsifying ideologically aligned hypotheses. For instance, many high-profile non-replications involve studies on social priming or implicit bias, topics often intertwined with progressive narratives, where flexible experimental designs allow multiple analytical paths that inflate false positives to support preconceived views. Models of in research propose that homogeneity in researcher worldviews leads to "," prioritizing novel, paradigm-affirming findings over rigorous testing, thereby exacerbating QRPs and contributing to the crisis's severity in these fields compared to more ideologically diverse or apolitical hard sciences. This pattern underscores credibility concerns in soft sciences, as institutional biases—evident in and funding priorities that favor results aligning with prevailing cultural assumptions—systematically undervalue null or contradictory , perpetuating a cycle of non-cumulative knowledge. Empirical reviews indicate that studies promoting themes were overrepresented among those failing replication, suggesting that ideological conformity, rather than neutral scientific norms, drives selective emphasis on weakly supported claims. Addressing the crisis thus requires not only preregistration and transparency reforms but also diversifying perspectives to mitigate the causal role of political in eroding epistemic rigor.

Policy and Societal Implications

Funding Priorities and Resource Allocation

Federal research funding in the United States allocates the majority of resources to hard sciences, reflecting priorities for fields with stronger empirical foundations and direct contributions to , outcomes, and . In 2022, federal obligations for totaled $45.4 billion, with approximately 84%—or $38 billion—directed toward life sciences ($19.05 billion), physical sciences ($9.45 billion), ($3.74 billion), geosciences/atmospheric/ sciences ($3.44 billion), and computer/ sciences ($2.30 billion). In contrast, only 7% ($3.06 billion) supported soft sciences, including ($2.29 billion) and other social sciences ($0.77 billion).
Field CategoryBasic Research Funding (FY 2022, $ billions)Percentage of Total Basic Research
Life Sciences19.0542%
Physical Sciences9.4521%
3.748%
Geosciences, Atmospheric, Ocean Sciences3.448%
Computer and Information Sciences2.305%
Hard Sciences Subtotal~38.084%
2.295%
Social Sciences0.772%
Soft Sciences Subtotal3.067%
Total45.4100%
This table illustrates the skewed distribution, where hard sciences dominate due to their capacity for controlled experimentation and quantifiable predictions, enabling clearer assessments of . Soft sciences, reliant on observational data and human subjects, face inherent complexities that reduce replicability and predictive accuracy, leading funders to view them as higher-risk investments. The , particularly acute in and related fields, has intensified scrutiny on soft science , as low rates—often below 50% in large-scale replication attempts—undermine claims of reliability and prompt reallocations toward disciplines with more robust verification methods. Grant cultures emphasizing novelty over replication further disadvantage soft sciences, where resource constraints limit large-scale, powered studies essential for . Within the , the Social, Behavioral, and Economic Sciences directorate exemplifies this, receiving roughly 3-4% of the agency's $9.54 billion FY 2023 appropriation—around $300 million—compared to over 20% each for and biological sciences directorates. Globally, similar patterns hold, with sciences securing about 770% more grant than social sciences over the past three decades, driven by demands for interdisciplinary applications tied to practical goals like or . These priorities underscore a commitment to causal realism in distribution, favoring fields where interventions yield verifiable, scalable impacts over those prone to interpretive variability and ideological influences. Critics from soft advocates argue for parity to address societal challenges, but of differential rigor supports the status quo, as hard outputs consistently demonstrate higher rates of translation to real-world advancements.

Impact on Public Trust and Decision-Making

The distinction between hard and soft sciences contributes to varying levels of public confidence, with hard sciences generally enjoying higher trust due to their emphasis on replicable experiments and quantitative predictability, while soft sciences face skepticism stemming from lower reproducibility rates and interpretive flexibility. A 2019 study found that informing participants about replication failures in psychological research—a field classified as soft science—significantly reduced public trust in both past and future findings from the discipline, with trust levels dropping even after disclosures of methodological reforms. This erosion is exacerbated by the replication crisis, which has highlighted systemic issues in soft sciences like psychology and economics, where initial high-profile results often fail to hold under scrutiny, leading to perceptions of unreliability among lay audiences. In , reliance on soft sciences for amplifies risks, as their findings influence areas such as , , and social welfare, where causal inferences are harder to establish definitively. For instance, policies derived from non-replicable studies have informed interventions like certain programs, but subsequent failures to replicate core effects—such as those in applications—have undermined efficacy and public faith in evidence-based . Ideological biases prevalent in soft science institutions, including a documented left-leaning skew in academia, further complicate trust by introducing selective emphasis on findings that align with prevailing narratives, as seen in politicized interpretations of inequality or behavioral data that prioritize equity over empirical rigor. This has contributed to diverging trust trends, with conservative-leaning publics expressing lower confidence in overall since the 1990s, partly attributing it to perceived overreach in policy applications from ideologically influenced soft fields. Efforts to restore trust require prioritizing hard methodologies in policy-relevant soft research, such as enhanced replication mandates, yet persistent challenges like p-hacking and continue to hinder progress. from Pew Research indicates that while overall trust in scientists remains relatively high, skepticism grows when soft outputs inform contentious decisions, such as guidelines or economic forecasts, underscoring the need for transparent epistemic standards to mitigate backlash. Ultimately, without addressing these disparities, public decision-making risks being swayed by provisional claims masquerading as settled knowledge, perpetuating cycles of disillusionment.

References

  1. https://scholar.harvard.edu/files/shapin/files/hs_final_online.pdf
  2. https://journals.sagepub.com/doi/abs/10.1177/00732753221094739
  3. https://www.nature.com/articles/s44271-023-00003-2
  4. https://www.usu.edu/today/story/whats-the-gripe-between-hard-and-soft-sciences-the-debate-rages-on
  5. https://plato.stanford.edu/entries/scientific-unity/
  6. https://simplicable.com/science/hard-science
  7. https://pmc.ncbi.nlm.nih.gov/articles/PMC2850928/
  8. https://www.impactio.com/blog/an-analysis-of-the-the-differences-between-hard-and-soft-sciences
  9. https://pmc.ncbi.nlm.nih.gov/articles/PMC3904890/
  10. https://www.thoughtco.com/hard-vs-soft-science-3975989
  11. https://www.researchgate.net/publication/232467869_How_Hard_Is_Hard_Science_How_Soft_Is_Soft_Science
  12. https://onlinelibrary.wiley.com/doi/10.1111/j.1465-7295.1980.tb00568.x
  13. https://escholarship.org/content/qt8cr8x1c4/qt8cr8x1c4_noSplash_c76be0091bea62dcb37aef63b85e16ae.pdf
  14. https://neurobirdies.com/is-neuroscience-a-hard-or-soft-science/
  15. https://plato.stanford.edu/entries/aristotle/
  16. https://academyofideas.com/2014/01/aristotles-divisions-of-science/
  17. https://plato.stanford.edu/entries/comte/
  18. https://www.isko.org/cyclo/comte
  19. https://plato.stanford.edu/entries/mill/
  20. https://www.laits.utexas.edu/poltheory/mill/sol/sol.b06.c04.html
  21. https://plato.stanford.edu/entries/dilthey/
  22. https://www.marxists.org/reference/subject/philosophy/works/ge/dilthey1.htm
  23. https://gwern.net/doc/psychology/1957-osgood-themeasurementofmeaning.pdf
  24. https://pmc.ncbi.nlm.nih.gov/articles/PMC198502/
  25. https://philosophynow.org/issues/45/After_the_Science_Wars
  26. https://www.science.org/doi/10.1126/science.aac4716
  27. https://www.washingtonpost.com/opinions/congress-should-cut-funding-for-political-science-research/2012/06/04/gJQAuAJMEV_story.html
  28. https://www.[forbes](/page/Forbes).com/sites/marshallshepherd/2022/08/17/its-time-to-retire-the-terms-hard-and-soft-science/
  29. https://www.scribbr.com/methodology/controlled-experiment/
  30. https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
  31. https://www.researchgate.net/publication/332487712_Quantification_and_its_importance_to_modeling_in_introductory_physics
  32. https://plato.stanford.edu/entries/scientific-reproducibility/
  33. https://pmc.ncbi.nlm.nih.gov/articles/PMC9670254/
  34. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=15046&context=libphilprac
  35. https://methods.sagepub.com/ency/edvol/sage-encyc-qualitative-research-methods/chpt/observational-research
  36. https://academic.oup.com/edited-volume/41258/chapter/350832844
  37. https://researchmethods.middcreate.net/modules/qualitative-data-analysis/approaches-and-methods/types-of-interpretive-analysis/
  38. https://anthropology.ua.edu/theory/symbolic-and-interpretive-anthropologies/
  39. http://www.velthuis.dds.nl/velthuis_rae_meanings%2520prices.pdf
  40. https://www.studysmarter.co.uk/explanations/anthropology/anthropological-theory/interpretive-anthropology/
  41. https://www.simplypsychology.org/observation.html
  42. https://opentext.wsu.edu/carriecuttler/chapter/observational-research/
  43. https://www.tutor2u.net/psychology/reference/observational-techniques-strengths-and-limitations
  44. https://quizlet.com/study-guides/advantages-and-disadvantages-of-observational-research-b2205d1e-3213-4735-84b0-998b1c3d419a
  45. https://pmc.ncbi.nlm.nih.gov/articles/PMC3818421/
  46. https://www.vox.com/science-and-health/2018/8/27/17761466/psychology-replication-crisis-nature-social-science
  47. https://zipdo.co/replication-statistics/
  48. https://psycnet.apa.org/record/1987-26934-001
  49. https://errorstatistics.com/wp-content/uploads/2015/04/meehl-1978.pdf
  50. https://content.csbs.utah.edu/~cashdan/fieldmeth/diamond_soft_sciences.pdf
  51. https://www.forbes.com/sites/marshallshepherd/2022/08/17/its-time-to-retire-the-terms-hard-and-soft-science/
  52. https://www.degruyterbrill.com/document/doi/10.7208/9780226496047-003/html
  53. https://www.bmartin.cc/pubs/88psa2/Dugdale.pdf
  54. https://www.sciencedirect.com/science/article/abs/pii/S0022103121001372
  55. https://www.researchgate.net/publication/355650075_Gender_representation_cues_labels_of_hard_and_soft_sciences
  56. https://www.researchgate.net/publication/50978952_Causal_Realism
  57. https://philsci-archive.pitt.edu/8545/1/CausalReal2011.pdf
  58. https://pmc.ncbi.nlm.nih.gov/articles/PMC2836213/
  59. https://causalai.net/r60.pdf
  60. https://www.cambridge.org/core/journals/philosophy-of-science/article/causal-realism-and-the-laws-of-nature/ABD82E8B8A55C6021EDE29D1AC3D01EB
  61. https://pmc.ncbi.nlm.nih.gov/articles/PMC12513368/
  62. https://bayes.cs.ucla.edu/BOOK-2K/woodward-rev.pdf
  63. https://pmc.ncbi.nlm.nih.gov/articles/PMC7801307/
  64. https://www.researchgate.net/publication/238278455_Comparing_the_Epistemologies_of_Scientific_Disciplines_in_Two_Distinct_Domains_Modern_Physics_versus_Social_Sciences_I_The_Epistemology_and_Knowledge_Characteristics_of_the_Physical_Sciences
  65. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.580111/full
  66. https://pmc.ncbi.nlm.nih.gov/articles/PMC7859482/
  67. https://doi.org/10.1126/science.aac4716
  68. https://www.nature.com/articles/s41562-023-01749-9
  69. https://doi.org/10.1371/journal.pone.0010068
  70. https://www.science.org/content/article/preregistering-transparency-and-large-samples-boost-psychology-studies-replication-rate
  71. https://newsroom.haas.berkeley.edu/research/amid-a-replication-crisis-in-social-science-research-six-year-study-validates-open-science-methods/
  72. https://pmc.ncbi.nlm.nih.gov/articles/PMC10241466/
  73. https://www.nas.org/academic-questions/31/2/homogenous_the_political_affiliations_of_elite_liberal_arts_college_faculty
  74. https://cms.ratio.se/app/uploads/2004/11/working-paper-no.-53.-klein-daniel-stern-charlotta-2004.-how-politically-diverse-are-the-social-sciences-and-humanities-survey-evidence-from-six-fields..pdf
  75. https://www.reddit.com/r/JordanPeterson/comments/ubfsl5/survey_76_of_academics_in_the_social_sciences_in/
  76. https://journals.sagepub.com/doi/10.1177/17456916241252085
  77. https://sites.rutgers.edu/lee-jussim/wp-content/uploads/sites/135/2020/04/Honeycutt-Jussim-2020-A-Model-of-Political-Bias-in-Social-Science-Research.pdf
  78. https://psycnet.apa.org/record/2024-24022-011
  79. https://www.bps.org.uk/research-digest/has-liberal-bias-psychology-contributed-replication-crisis
  80. https://www.psychologytoday.com/us/blog/rabble-rouser/202005/political-biases-in-academia
  81. https://ncses.nsf.gov/pubs/nsf24332
  82. https://www.sciencedirect.com/science/article/abs/pii/S221462962030298X
  83. https://pubmed.ncbi.nlm.nih.gov/28727961/
  84. https://undark.org/2017/09/11/replication-crisis-funding/
  85. https://www.nsf.gov/about/budget/fy2023/appropriations
  86. https://nsf-gov-resources.nsf.gov/about/budget/fy2023/pdf/74_fy2023.pdf
  87. https://www.tandfonline.com/doi/full/10.1080/02691728.2023.2245769
  88. https://www.quora.com/What-causes-the-mistrust-between-hard-sciences-and-social-sciences-in-academia
  89. https://www.tandfonline.com/doi/full/10.1080/23743603.2019.1684822
  90. https://www.bps.org.uk/research-digest/replication-crisis-lowers-publics-trust-psychology
  91. https://pubmed.ncbi.nlm.nih.gov/32036741/
  92. https://statmodeling.stat.columbia.edu/2023/06/23/its-worse-than-it-sounds-passive-corruption-in-the-social-sciences/
  93. https://www.psychologytoday.com/us/blog/rabble-rouser/202112/recognizing-politically-biased-social-science
  94. https://pmc.ncbi.nlm.nih.gov/articles/PMC12177194/
  95. https://www.sciencedirect.com/science/article/abs/pii/S2214804324001642
  96. https://www.pewresearch.org/science/2024/11/14/public-trust-in-scientists-and-views-on-their-role-in-policymaking/
  97. https://www.nature.com/articles/s41599-024-03909-2
Add your contribution
Related Hubs
User Avatar
No comments yet.