Bibliographic record
Abstract
European Journal of PersonalityVolume 14, Issue 5 p. 371-375 Editorial Editorial: personality and cognition Doug McCann, Corresponding Author Doug McCann dmccann@yorku.ca York University, Toronto, Ontario, CanadaDepartment of Psychology, York University, Toronto, Ontario, M3J 1P3, Canada.Search for more papers by this authorNorman S. Endler, Norman S. Endler York University, Toronto, Ontario, CanadaSearch for more papers by this author Doug McCann, Corresponding Author Doug McCann dmccann@yorku.ca York University, Toronto, Ontario, CanadaDepartment of Psychology, York University, Toronto, Ontario, M3J 1P3, Canada.Search for more papers by this authorNorman S. Endler, Norman S. Endler York University, Toronto, Ontario, CanadaSearch for more papers by this author First published: 01 December 2000 https://doi.org/10.1002/1099-0984(200009/10)14:5<371::AID-PER388>3.0.CO;2-YAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume14, Issue5Special Issue: Personality and CognitionSeptember/October 2000Pages 371-375 RelatedInformation
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.046 | 0.029 |
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".