Bibliographic record
Abstract
This thesis investigates three topics in theoretical and applied econometrics: Bartlett-type correction of the Distance Metric (DM) test, a Generalized Method of Moments (GMM) study of the effect of the North American Free Trade Agreement (NAFTA) on Quebec manufacturing industries, and a goodness-of-fit test for copulas. The first topic derives an Edgeworth approximation of the distribution of the DM test statistic and obtains a Bartlett-type correction factor, then it uses examples of covariance structures to illustrate the theoretical results and applies the theoretical results to study the covariance structure of earnings. The second topic calculates Canadian tariff rates over the period 1991-2007 for manufacturing industries, classified using the North American Industry Classification System (NAICS), proposes a simulation-based moment selection procedure to improve the properties of the system GMM estimator, and analyzes the effect of NAFTA on earnings of Quebec manufacturing industries. The third topic proposes a new rank-based goodness-of-fit test for copulas, conducts a power study to show that the new test has reasonable properties, and presents an application
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".