Bibliographic record
Abstract
Retirement plans around the world poured the collective savings of millions of employees into the common stock of banks, corporations, and Wall Street ventures that recklessly over-compensated Chief Executive Officers, engineered artificial short-term gains, and gambled fatally with risk. Why did this happen? The financial system needs the oversight of vigilant market participants, but in this case many pension funds, mutual funds, hedge funds, insurance funds, and other major investors were silent. Conventional investment theory about diversification also played a part; while intending to control specific risks, it had the unintended side effect of increasing risk overall. What are the root causes of these failures to exercise vigilance? Studies increasingly point to at least one important factor: flaws in investors’ own accountability. The essence of an effective financial system is that the entities in it, including pension funds, are responsible for their actions. Responsibility implies a willingness to be accountable, and that in turn requires an integrated, active approach to exercising shareowner stewardship. This article proposes a series of practical steps to that end.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".