Peer Review #2 of "Denouncing the use of field-specific effect size distributions to inform magnitude (v0.1)"
Bibliographic record
Abstract
An effect size (ES) provides valuable information regarding the magnitude of effects, with the interpretation of magnitude being the most important.Interpreting ES magnitude requires combining information from the numerical ES value and the context of the research.However, many researchers adopt popular benchmarks such as those proposed by Cohen.More recently, researchers have proposed interpreting ES magnitude relative to the distribution of observed ES values in a specific field, creating unique benchmarks for declaring effects small, medium or large.However, there is no valid rationale whatsoever for this approach.This study was carried out in two parts: 1) We identified articles that proposed the use of field-specific ES distributions to interpret magnitude (primary articles); and 2) We identified articles that cited the primary articles and classified them by year and publication type.The first type consisted of methodological papers.The second type included articles that interpreted ES magnitude using the approach proposed in the primary articles.There has been a steady increase in the number of methodological and substantial articles discussing or adopting the approach of interpreting ES magnitude by considering the distribution of ESs in that field, even though the approach is devoid of a theoretical framework.It is hoped that this research will restrict the practice of interpreting ES magnitude relative to the distribution of ES values in a field and instead encourage researchers to interpret such by considering the specific context of the study.
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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 | 0.172 | 0.627 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.090 | 0.042 |
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".