APA Publishing's Equity, Diversity, and Inclusion Toolkit for Journal Editors
Bibliographic record
Abstract
As a mission-driven organization that applies the best available psychological science to benefit society and improve lives, APA is committed to infusing the principles of equity, diversity, and inclusion (EDI) into all aspects of the work we do. As shepherds of psychology's science and practice, journal Editors are uniquely positioned to enable equitable and inclusive practices at every stage of the research and publication process. This toolkit offers more than 30 recommendations based on resources, standards, and initiatives available to Editors to support their efforts to encourage inclusive and equitable practices for their peer-reviewed journals.
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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 | 0.114 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.037 | 0.023 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.066 | 0.065 |
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".