Preliminary: Not to be quoted or cited 1 CORPORATE LINKAGES AND BANK LENDING IN CANADA: SOME FIRST RESULTS
Bibliographic record
Abstract
Economists have directed attention to the role of corporate governance in determining the performance of business enterprises well before the issue was thrust into prominence by the troubles of Enron, WorldCom and others. Much of this literature examines the consequences of different mechanisms for corporate control. An important part of this literature stresses the role of banks in corporate governance. Banks can participate and influence the governance of corporations through both a credit and a governance relation. The credit relation arises because banks in their role as delegated monitors serve as more than passive lenders and actively involve themselves in shaping the activities of borrowers through being screeners, monitors and enforcers of loans. Banks though the sum of their individual loan decisions collectively decide which corporate projects gain bank finance. The governance relation arises where, as in some countries, banks participate in the management of firms through shareholdings and voting powers that place bank representatives on corporate boards. Bank representatives may also serve 1 I am indebted to the Phillips Hager and North Centre for the Study of Financial Markets for its support in
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.098 |
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".