A General Approach to Panel Data Set-Theoretic Research
Why this work is in the frame
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Bibliographic record
Abstract
Academic research based on general linear statistical models has been rapidly moving toward a greater and richer use of longitudinal and panel data econometric methods. By contrast, set-theoretic empirical research, despite its growing diffusion, has been mainly focused on cross-sectional analysis to date. This article covers this void in panel data set-theoretic research. We provide some diagnostic tools to assess a set-theoretic consistency and coverage both cross-sectionally and across time. The suggested approach is based on the distinction between pooled, between and within consistency and coverage, which can be computed using panel data. We use KLD’s panel (1991–2005) to illustrate how the proposed approach can be applied in the context of set-theoretic longitudinal research.
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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 | 0.040 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.010 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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