Scholarship, not politics: Reply to Eidelson (2025).
Bibliographic record
Abstract
Eidelson's (2025) commentary misses the point of our article (Walker et al., 2025), which reviews the history of antisemitism within the psychology profession and calls for the American Psychological Association to acknowledge its past and to proactively address the recent rise in antisemitism. Our scholarship is consistent with that of others in the field (e.g., Winston, 2020). We refute some of the commentary's (Eidelson, 2025) specific misinterpretations of statistics we cite and mention recent studies related to the negative psychological impact of antisemitic campus activism on a significant subset of Jewish students. Eidelson's focus on our choice of the International Holocaust Remembrance Alliance definition of antisemitism, his focus on what he thinks of as our failure to condemn Israel, and his mistaken discrediting of the Federal Bureau of Investigation's Uniform Crime Report statistics reported in our article obscure the central goal of the article, thus politicizing the issue rather than furthering scholarship in the area. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.050 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".