Bibliographic record
Abstract
This thesis consists of three essays.The first essay (chapter two) examines the impact of Canadian financial restatements on market quality.We find that Canadian financial restatements announced during 1997-2006 signal to market participants that expected future cash flows and their uncertainty are diminished and increased, respectively, and that they affect the market quality for restating firms.Abnormal returns are not only related to downward revisions in the consensus earnings forecasts of analysts but they become more negative for firms cross-listed in the U.S., and for revenue recognition and company-initiated restatements.Total residual volatility and its information-based permanent component from a GARCH model with an asymmetric effect and the adverse selection spread component increase following such announcements.Relative spreads and a spread-depth market-quality index not only increase (decrease) following such announcements but are lower (higher) for firms cross-listed in the U.S. Relative spreads (unlike the market-quality index) remain higher post-announcement, and are lower after the 2002 enactment of the Sarbanes-Oxley Act.Relative spreads, Amihud illiquidity estimates, synchronicity and volatilities increase for revenue recognition restatements.The second essay (chapter three) examines the link between Canadian financial restatements and corporate governance.Using a novel, hand-collected dataset of corporate governance characteristics for a matched sample of 177 restating and 177 control firms, we find that Canadian firms are less likely to restate when they have bigger blockholder and management ownerships, audit committees with at least one director with financial expertise, a lower leverage ratio, and when they use a big 5 auditor.Restatement likelihood is not related to the proportion of unrelated directors, and whether the CEO is the Board Chair or belongs to the founding family.CEO, President, CFO and external auditor turnover are significantly higher for restating firms compared Dr. Simon Lalancette, and the external examiners, Dr. Bryan Campbell, and Dr. Issouf Soumare for their invaluable suggestions, comments and feedback.I would also like to thank Dr. Sandra Betton for helping me with the initial data extraction.I would like 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".