CROSS-SECTIONAL INFERENCE BASED ON
Bibliographic record
Abstract
This paper focuses on cross-sectional inference based on data from a longitudinal survey which carries some additional components to achieve cross-sectional representativity. When inferring about the differences in the cross-sectional populations at two different points in time, problems arise with variance estimation for the difference of the respective estimates, when the estimates are derived from such a survey. There are several factors contributing to these problems. Of these, the most important is the sample overlap at the two time points due to the underlying longitudinal survey design; this introduces a strong covariance component which must be included in the estimate of the variance of the difference. Also associated with the underlying longitudinal sample is the complexity introduced by longitudinally sampled individuals moving from one geographical part of the country to another, and thus being used to represent a different part of the cross-sectional population than that for which they were selected. The degree of complication that such factors introduce to the variance estimation problem is determined by the manner in which the longitudinal sample has been supplemented and adjusted in order to attain cross-sectional samples and by the available design information that may be used for cross-sectional inference. The variance estimation problem is addressed for Canada’s Survey of Labour and Income Dynamics (SLID) within a
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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 | 0.051 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".