Replicating Empirical Research In Behavioral Ecology: How And Why It Should Be Done But Rarely Ever Is
Bibliographic record
Abstract
That empirical evidence is replicable is the foundation of science. Ronald Fisher a founding father of biostatistics, recommended that a null hypothesis be rejected more than once because "no isolated experiment, however significant in itself can suffice for the experimental demonstration of any natural phenomenon" (Fisher 1974:14). Despite this demand, animal behaviorists and behavioral ecologists seldom replicate studies. This practice is not part of our scientific culture, as it is in chemistry or physics, due to a number of factors, including a general disdain by journal editors and thesis committees for unoriginal work. I outline why and how we should replicate empirical studies, which studies should be given priority, and then elaborate on why we do not engage in this necessary endeavor. I also explain how to employ various statistics to test the replicability of a series of studies and illustrate these using published studies from the literature.
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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 | 0.057 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".