Should We Strive to Make Science Bias-Free? A Philosophical Assessment of the Reproducibility Crisis
Bibliographic record
Abstract
Recently, many scientists have become concerned about an excessive number of failures to reproduce statistically significant effects. The situation has become dire enough that the situation has been named the 'reproducibility crisis'. After reviewing the relevant literature to confirm the observation that scientists do indeed view replication as currently problematic, I explain in philosophical terms why the replication of empirical phenomena, such as statistically significant effects, is important for scientific progress. Following that explanation, I examine various diagnoses of the reproducibility crisis, and argue that for the majority of scientists the crisis is due, at least in part, to a form of publication bias. This conclusion sets the stage for an assessment of the view that evidential relations in science are inherently value-laden, a view championed by Heather Douglas and Kevin Elliott. I argue, in response to Douglas and Elliott, and as motivated by the meta-scientific resistance scientists harbour to a publication bias, that if we advocate the value-ladenness of science the result would be a deepening of the reproducibility crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.581 | 0.683 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.008 | 0.108 |
| Scholarly communication | 0.022 | 0.057 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".