Bibliographic record
Abstract
Arie W. Kruglanski is Distinguished University Professor of Psychology at the University of Maryland. He is the recipient of several awards including the National Institute of Mental Health Research Scientist Award, the Distinguished Scientific Contribution Award from the Society of Experimental Social Psychology and the Donald Campbell Award for Outstanding Contributions to Social Psychology from the Society for Personality and Social Psychology. He is Fellow of the American Psychological Association and the American Psychological Society, and presently serves as co-founder and senior investigator at the National Center for the Study of Terrorism and the Response to Terrorism. His research interests are in the domains of human judgment and decision making, the motivation-cognition interface, group and intergroup processes, the psychology of human goals, and the social psychological aspects of terrorism. Jocelyn J. Bélanger is Assistant Professor of Psychology at New York University Abu Dhabi. His research seeks to understand why, and under which circumstances, individuals are willing to sacrifice their lives for a cause. Dr. Bélanger is the architect behind Montreal’s Centre for the Prevention of Radicalization Leading to Violence. He also trains psychologists and social workers on the rehabilitation and reintegration of violent extremist offenders. Dr. Bélanger is the recipient of several awards such as the APA Dissertation Research Award and the Guy Bégin Award for the Best Research Paper in Social Psychology.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.524 | 0.424 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".