Quasi-Replication and the Contract of Error: Lessons from Sex Ratios, Heritabilities and Fluctuating Asymmetry
Bibliographic record
Abstract
▪ Abstract Selective reporting—e.g., the preferential publication of results that are statistically significant, or consistent with theory or expectation—presents a challenge to meta-analysis and seriously undermines the quest for generalizations. Funnel graphs (scatterplots of effect size vs. sample size) help reveal the extent of selective reporting. They also allow the strength of biological effects to be judged easily, and they reaffirm the value of graphical presentations of data over statistical summaries. Funnel graphs of published results, including: (a) sex-ratio variation in birds, (b) field estimates of heritabilities, and (c) relations between fluctuating asymmetry and individual attractiveness or fitness, suggest selective reporting is widespread and raise doubts about the true magnitude of these phenomena. Quasireplication—the “replication” of previous studies using different species or systems—has almost completely supplanted replicative research in ecology and evolution. Without incentives for formal replicative studies, which could come from changes to editorial policies, graduate training programs, and research funding priorities, the contract of error will continue to thwart attempts at robust generalizations. “For as knowledges are now delivered, there is a kind of contract of error between the deliverer and the receiver: for he that delivereth knowledge desireth to deliver it in such a form as may be best believed, and not as may be best examined; and he that receiveth knowledge desireth rather present satisfaction than expectant inquiry; and so rather not to doubt than not to err: glory making the author not to lay open his weakness, and sloth making the disciple not to know his strength.” The Advancement of Learning, Francis Bacon, 1605 ( 8 :170–171)
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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 | 0.600 | 0.844 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".