Bibliographic record
Abstract
Baltimore, Maryland. We thank our conference paper discussant, Denton Vaughan, and others attending the session for their comments and suggestions during and after the conference. To Larry Radbill we extend our sincere gratitude for his diligence and insight regarding the creation of the Basic Needs Module of the Survey of Income and Program Participation. To Gordon Fisher we extend our thanks for loaning us parts of his library, encouraging us constantly along the way, and providing comments on drafts of the paper. We also thank Arie Kapteyn and Klaas de Vos for their comments on our work and guidance and support throughout the project as we followed the methods of the Leyden group and the generations that follow. To Susan Poulin we owe special thanks for discussions concerning the Statistics Canada study in which the same basic methods as ours are used. We thank Linda Stinson, Clyde Tucker, and others within the Bureau of Labor Statistics for their guidance in exploring the “whys ” and “hows ” of minimum income and spending. Thanks also go to Teague Ruder for research assistance, and David
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.728 | 0.645 |
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".