Determinants of Board Size: A Longitudinal Analysis with 194 Firms Listed on the B3 S/A
Bibliographic record
Abstract
The research aims to analyze determinants of board size (BD) of companies listed on B3 S/A, from 2014 to 2019, with data collected by the Com.dinheiro.com platform. The hypotheses “firm size is positively related to board size” (H1); “firm ownership structure is negatively related to board size” (H2); and, “firm performance is positively related to board size” (H3), were quantitatively analyzed by multiple linear regression, heteroscedasticity and multicollinearity tests and F-statistics, based on the variables: company size (TAMA); ownership structure; type of control; performance (EBITDA), and control variable, gender of the board of directors and the year. The results indicate that board size was explained by company size, ownership structure, and performance, confirming the three proposed hypotheses. For future research we suggest the use of other dependent variables that portray the board structure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".