Bibliographic record
Abstract
This study examines the relationship between institutional ownership and firm performance using a sample of 567 Canadian firms in 2011. The focus on the Canadian firms provides additional insight towards the topic of institutional ownership as a remedial measure towards agency problems, since Canada has shared legal traditions with the United States, but has ownership concentration more comparable to levels in Western Europe and Asia. A distinguishing feature of this study's analysis involves the consideration of institutional investor by type as well as the inclusion of the number of such investors as a measure of ownership. \n \nThe effects of institutional ownership on performance measures Tobin's Q, Industry-Adjusted Tobin's Q, and Return on Assets are estimated using ordinary least squares (OLS) and two-stage least squares (2SLS) methodology, where the latter is employed to offset the endogeneity bias to which the OLS method is susceptible. Although several relationships emerged between institutional ownership levels and measures of Tobin's Q in the OLS regression, only a negative relationship between both the percentage and the number of insurance company investors, was observed to be significant once estimated simultaneously under the 2 SLS method. For all measures of performance, Hausman tests reveal that OLS results are biased in multiple instances; meaningful interpretation must rely on the 2 SLS results.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".