Social Psychology, Social Issues, and Social Policy: What Have We Learned?
Bibliographic record
Abstract
In this reflection on our term as coeditors of Social Issues and Policy Review (SIPR), we consider what we have learned from our work on the journal and what challenges lie ahead. We suggest that SIPR has been successful as a platform for work demonstrating the relevance of psychological research to issues of concern to policy makers and to the general public. It has been less effective, however, in its goal of stimulating more scholars in the discipline to engage in socially relevant research. We suggest that the current reward system within our discipline is not conducive to research that addresses broad societal issues, and that the emphasis on internal validity has limited the focus of our work. We call on psychologists to bridge micro and macro levels of analysis and to take their rightful place among those making a difference in the world.
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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 | 0.127 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.038 | 0.050 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".