How do prudential regulators discuss and mitigate short-termism?
Bibliographic record
Abstract
The Global Financial Crisis (GFC) of 2007-09 has been correlated with excessive risk-taking and disaster myopia in financial firms. Responses to those forms of short-termism include remuneration principles, attention to culture in firms, and upgraded corporate governance requirements. This thesis provides a cohesive analysis of regulatory responses to short-termism focused on the voice of prudential regulators. The method is structured, focused comparison of prudential regulators public messaging across four jurisdictions - Australia, Canada, Ireland and the United Kingdom - from 2008 to 2018. The thesis confirms and expands elements from Dallas's (2012) framework of information problems, structural problems and individual incentives as causes of short-termism. The thesis finds regulators discussing the components as forming a cohesive whole rather than as discrete elements, consistent with prior research that characterises financial markets as a complex adaptive system. Regulators usually justify the components by referring to international peers, and this thesis recommends they could broaden their sources of knowledge to consider lessons from other complex adaptive systems.
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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".