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About the Authors

2019· other· en· W4396897674 on OpenAlexaboutno aff
Arie W. Kruglanski, Jocelyn J. Bélanger, Rohan Gunaratna

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Extract 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.125
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0910.041

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.

Opus teacher head0.022
GPT teacher head0.333
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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