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
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".