Bibliographic record
Abstract
This thesis considers the econometric problem of endogeneity in an accelerated life regression model.The proposed instrumental variables inference, based on inverting a pivotal statistic, is exact regardless of instrument quality.A (i) least squares statistic and (ii) distribution-free linear rank statistic allowing censoring are provided.A simulation confirms that the quality of exogenous variation determines an instrument's informative content.An original prospectively collected observational data set provides an empirical illustration, in which, the trauma status of a pediatric critical care patient instruments a possibly confounded illness severity index in a length of stay regression for a specific pediatric intensive care population.Results suggest a clinically relevant bias correction for routinely collected patient risk indices that is meaningful for informing policy in the health care setting.Very rarely does one have the opportunity to pursue two life dreams.This thesis marks the culmination of an incredible personal journey that has allowed me to interact and learn from some amazing people without whom it would never have materialized.The first and foremost person that I must thank is my supervisor, Lynda Khalaf.From the first course she taught me to my thesis completion, she never waivered in her encouragement, her dedication and her belief that I could actually do this.I am convinced that her attention to detail and support for her graduate students is second to none.Next, I wish to
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".