Replicating Empirical Research In Behavioral Ecology: How And Why It Should Be Done But Rarely Ever Is
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it