A General Approach to Panel Data Set-Theoretic Research
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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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 | 0.036 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".