Bibliographic record
Abstract
The first several chapters of this book were introductory in style, though with more attention to the subtleties of practical application than is common in elementary treatments. The simple models considered here are somewhat restricted in their range of application, because they are based on assumptions of independence and stationarity which will not nearly always be true. The last several chapters treated topics that have traditionally been thought of as more advanced. We hope this has given readers a taste of models for data which can be applied in a wide variety of situations. Models that are not strictly correct, or even perhaps badly broken, may nevertheless be useful, giving acceptably accurate predictions. The validity of model assumptions remains an important issue. We need to know the limits of the usefulness of our models. Comparing results from a simple model with results from a model that takes better account of the data can help in developing intuition. Somewhat ironically, Chapters 7-13 could be viewed as essential background for those who hope to do a good job of the analyses described in Chapters 3-6! An understanding of multi-level and repeated measures models seems particularly important, since these models bring attention to structure in the noise component which must be accounted for in a proper analysis.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.349 | 0.144 |
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".