Commentaries and Rejoinder on
Bibliographic record
Abstract
While direct replications such as the “Many Labs” project are extremely valuable in testing the reliability of published findings across laboratories, they reflect the common reliance in psychology on single vignettes or stimuli, which limits the scope of the conclusions that can be reached. New experimental tools and statistical techniques make it easier to routinely sample stimuli, and to appropriately treat them as random factors. We encourage researchers to get into the habit of including multiple versions of the content (e.g., stimuli or vignettes) in their designs, to increase confidence in cross-stimulus generalization and to yield more realistic estimates of effect size. We call on editors to be aware of the challenges inherent in such stimulus sampling, to expect and tolerate unexplained variability in observed effect size between stimuli, and to encourage stimulus sampling instead of the deceptively cleaner picture offered by the current reliance on single stimuli.
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.
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 | 0.016 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.067 | 0.073 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".