Bibliographic record
Abstract
Abstract Examples of repeated cross‐sections (RCS) include daily tracking polls of political opinions during campaigns, monthly Current Population Surveys of unemployment, yearly national health interview surveys, and quadrennial election studies of presidential voting. Each iteration is a distinct sample, as opposed to panels in which the same people are interviewed two or more times. By asking the same questions on repeated survey samples from the same population, RCS studies allow us to track trends and to establish causal inferences. One analytic challenge is to maintain both the representativeness and the comparability of samples as fieldwork methods or sources change. The longer the span covered by an RCS, the likelier it is that the universe will change. For an RCS spanning decades, populations can change in fundamental ways. The universe of content also changes, as issues of one period are redefined or even rendered irrelevant in another. Extracting trends from RCS data typically requires smoothing to separate signal from noise, especially where samples or subsamples are small, but this can lead to bias due to excessive smoothing or to mistaking noise for signal because of sampling variability when there is not enough smoothing. By deploying time the RCS design enables certain kinds of causal inference, but many alternative micro‐processes are observationally equivalent, and so the RCS benefits from being combined with the panel design.
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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 | 0.061 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".