Bibliographic record
Abstract
The first essay in this dissertation uses US iron and steel shipbuilding data used by Thompson (2005).It proposes a Two-Stage Discrete Finite Mixture hazard model to account for selection effect associated with a high first-year exit rate, and omitted variable bias associated with missing information such as a shipbuilder's pre-entry experience.In the first stage, the model uses a Probit model to explain the selection effect by employing both a firm's production share and productionselection component at the time of entry as exclusion restrictions.The results identify two latent classes as proxies for pre-entry experience used in the Weibull model by Thompson.The model proposed is useful when important factors for the survival of To appreciate the glory of the universe and its offerings, you may begin by praising those who open their hearts so that you might grow; those who never expect you to compensate them; those in whose debt you remain forever: your teachers and your parents.I am grateful to my thesis supervisor, Professor Marcel Voia, for his honest academic support, his patience, and his deep knowledge of empirical econometrics, and to thesis committee member Dr. Kim Huynh from the Bank of Canada for contributing far more than his role required
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".