Board Size, CEO Duality, and the Value of Canadian Manufacturing Firms
Bibliographic record
Abstract
The purpose of this study is to examine the impact of board size and the CEO (Chief Executive Officer) duality on the value of Canadian manufacturing firms. A sample of 91 Canadian manufacturing firms listed on Toronto Stock Exchange (TSX) for a period of 3 years [from 2008-2010] was selected. The co-relational and non-experimental research design was used to conduct this study. The empirical results show that larger board size (large number of directors) has a negative impact on the value of Canadian manufacturing firms. The findings also show that the CEO duality has a positive impact on the value of Canadian manufacturing firms. In addition, firm size, firm performance, and potential growth of the firm positively impact on the value of Canadian manufacturing firms. This study contributes to the literature on the factors that affect value of the firm. The findings may be useful for the financial managers, investors, and financial management consultants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".