Debunking the Myth of Value-Neutral Virginity: Toward Truth in Scientific Advertising
Bibliographic record
Abstract
The scientific community often portrays science as a value-neutral enterprise that crisply demarcates facts from personal value judgments. We argue that this depiction is unrealistic and important to correct because science serves an important knowledge generation function in all modern societies. Policymakers often turn to scientists for sound advice, and it is important for the wellbeing of societies that science delivers. Nevertheless, scientists are human beings and human beings find it difficult to separate the epistemic functions of their judgments (accuracy) from the social-economic functions (from career advancement to promoting moral-political causes that "feel self-evidently right"). Drawing on a pluralistic social functionalist framework that identifies five functionalist mindsets-people as intuitive scientists, economists, politicians, prosecutors, and theologians-we consider how these mindsets are likely to be expressed in the conduct of scientists. We also explore how the context of policymaker advising is likely to activate or de-activate scientists' social functionalist mindsets. For instance, opportunities to advise policymakers can tempt scientists to promote their ideological beliefs and values, even if advising also brings with it additional accountability pressures. We end prescriptively with an appeal to scientists to be more circumspect in characterizing their objectivity and honesty and to reject idealized representations of scientific behavior that inaccurately portray scientists as value-neutral virgins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".