Intellectual capital myths: Comments on literature review
Bibliographic record
Abstract
This article contributes to the growing body of literature exploring the important role that information transparency plays in strengthening the national corporate governance regime. We review the 2007 amendments to the Canadian reporting legislation with the particular emphasis on sections pertaining to executive compensation and boards of directors. Taking into consideration the specificities of the „comply-or-explain‟ system in Canada, we seek to uncover the extent to which publicly-listed firms comply with these newly amended standards of corporate governance reporting. Based on a comparison of 403 proxy circulars issued in the post-amendment period, we identified important cross-firm variations in the type and format of disclosed information on executive compensation and corporate boards of directors. In order to address the problems that inter-organizational disclosure discrepancies generate for governance researchers and analysts, we provide several recommendations on how Canadian publicly-traded companies can improve their reporting practices.
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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 | 0.040 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".