Using Event-history Analysis: Lessons from Fifteen Years of Practice
Bibliographic record
Abstract
Innovative statistical methods and new longitudinal surveys paved the way to the widespread use of event-history analysis in social science during the last two decades. This paper does not attempt to provide a comprehensive review of these innovative methods. More modestly, it aims at identifying and describing the problems encountered by two privileged users. Two types of problems are discussed here. The first arises from the design of the surveys, or the way data are collected, and the difficulty to test specific hypotheses with the existing databases; this is the kind of problem that Le Bourdais has faced in analysing family dynamics. The second has to do with the limitations of the survival regression models when the longitudinal phenomena studied can no longer properly be thought of as a small number of unique events; this is the type of problem encountered by Renaud in his ten-year Quebec panel survey of new immigrants.
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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 | 0.211 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".