Channels of Convergence: Investor Engagement and Interlocked Directorates
Bibliographic record
Abstract
In a setting of globalized financial capitalism an issue which has received little attention to date is not whether national models of corporate governance are converging or diverging but rather the channels by which market and regulatory forces interact to promote such convergence or divergence. I herein propose the institutional investor/interlocked directorate pipeline as one such channel of potential convergence. In theory, institutional investors impart tacit knowledge to a select group of corporations by means of engagement activities; such knowledge and influence can subsequently be distributed to a wider corporate population by means of interlocked directorates originating from the engaged corporations. An analysis of the distribution of these pipelines across Canada demonstrates that such pipelines can penetrate geopolitical and industrial boundaries and interact with regulatory forces in shaping models of corporate governance. The wide distribution of such pipelines effectively transforms tacit knowledge originally conveyed by institutional investors to a geographically limited sample of corporations into explicit knowledge accessible across the country.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".