Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.004 |
| 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) | 0.010 | 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 it