Corporate Governance: A Comparative Study of Practices in Turkey and Canada
Bibliographic record
Abstract
The main objective of this study is to analyze the development of corporate governance in Turkey, particularly after 2006 comparing it with Canada, a country reputed to have one of the best corporate governance systems in the world. Through the process of comparison, the overall goal of the study is to identify current strengths and weaknesses of the Turkish system and to determine whether Turkey is moving forward faster in terms of corporate governance practices than Canada. The study shows that Turkey has improved its corporate governance continuously with extremely quick development of many aspects of modern corporate governance. Development of effective and efficient boards, on the other hand, represents a variable that slows down this progress. Also the claim that “developing countries are closing the gap they have in terms of corporate governance with developed countries” finds support. Another significant finding is that although there was no change in Turkey’s positive trend in corporate governance development during the 2008-2009 financial crisis, Canada’s corporate governance practice and reputation were negatively affected in a notable way during this period. It is concluded that researchers and practitioners should give special attention to board development and its functioning in order to develop corporate governance in Turkey, and also in Canada, because this factor is found to be weak compared to other factors in Turkey and Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".