GIVING VOICE TO REALITY: MICHAEL TREBILCOCK AND PENSION GOVERNANCE ISSUES
Bibliographic record
Abstract
This article explores how Michael Trebilcock's multidimensional and pragmatic approach to the governance issues arising from limited liability in corporate law and the problems of assigning the task of protecting corporate employees to private contracting have widespread application in many areas where parties’ relationships are not confined to a single isolated transaction, including employment-based pension plans. Observations about the potential for opportunistic behaviour in the endgame of a long-term contractual relationship and the moral hazards that accompany limited liability for corporate managers are extended into the problems of pension-plan governance. The sources of moral hazards in funding assumptions and the agency costs in pension investment policy decisions are analysed and illustrated. The article suggests a role for the ‘voice’ of plan members in the governance of pension plans as a means of controlling conflicts of interest and moral hazard in these relationships, echoing some of Trebilcock's suggestions for addressing such conflicts in other aspects of the employment relationship.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".