Award for Distinguished Contributions to the International Advancement of Psychology: Klaus Boehnke.
Bibliographic record
Abstract
The Award for Distinguished Contributions to the International Advancement of Psychology is given to individuals who have made sustained and enduring contributions to international cooperation and the advancement of knowledge in psychology. The 2022 recipient of the APA Award for Distinguished Contributions to the International Advancement of Psychology was selected by the 2021 Committee on International Relations in Psychology. The committee is pleased to present the 2022 APA Award for Distinguished Contributions to the International Advancement of Psychology to Klaus Boehnke. Dr. Boehnke is a professor of Social Science Methodology at Jacobs University, Bremen, Germany, and deputy head of the Center for Sociocultural Research at the National Research University Higher School of Economics in Moscow, Russian Federation. He has lived and worked at universities in Singapore, Australia, and Canada, in addition to his native Germany. His scholarship has included substantial contributions in political psychology, peace psychology, the psychology of youth, and innovative methodologies and frameworks for cross-national studies. He works with partners and colleagues around the world. Dr. Boehnke has been instrumental in creating and serving in institutions and organizations that promote cooperation across borders. He has served on boards and as advisor to multiple international groups and committees, including as president of the International Association for Cross-Cultural Psychology, one of the most internationally diverse psychological associations worldwide. (PsycInfo Database Record (c) 2023 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.246 | 0.187 |
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".