Bibliographic record
Abstract
Purpose This paper aims to investigate the impact of the characteristics of two corporate governance mechanisms, namely, board of directors and audit committee (hereafter AC), on the level of compliance with International Financial Reporting Standard [hereafter International Financial Reporting Standards (IFRS)] 7 “Financial instruments: Disclosures” (hereafter FID). Design/methodology/approach Using a self-constructed checklist of 128 items, this research measures the compliance with IFRS 7 of 63 Canadian financial institutions listed on the Toronto Stock Exchange during a period of three years (2014-2016). Fixed effect panel regressions have been used to capture the individual effect present in authors’ data. Findings Empirical results show that the mean compliance level with IFRS 7 requirements is about 77 per cent and identify various areas of non-compliance. This level of compliance has a positive linkage with the board size and independence. Similarly, the AC independence and financial accounting expertise are shown to positively affect authors’ dependent variable. Nevertheless, CEO/chairman duality, AC size and meeting frequency are not significantly correlated with the level of compliance with IFRS 7. Originality/value This study expands prior compliance literature in the Canadian setting by examining the determinants of compliance with IFRS mandatory disclosures. Also, and to the best of the authors’ knowledge, this paper is among the first studies that have investigated the effect of corporate governance characteristics (hereafter CGC) on compliance with all IFRS 7 requirements in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".