Bibliographic record
Abstract
Agency problems in economics virtually always entail self-interested agency exhibiting "insufficient" loyalty to principal.Social psychology also has a literature, mainly derived from work by Stanley Milgram, on issues of agency, but this emphasizes excessive loyalty -people undergoing a so-called "agentic shift" and forsaking rationality for loyalty to a legitimate principal, as when "loyal" soldiers obey orders to commit atrocities.This literature posit that individuals experience a deep inner satisfaction from acts of loyalty -essentially a "utility of loyalty" -and that this both buttresses institutions organized as hierarchies and explains much human misery.Agency problems of excessive loyalty, as when boards kowtow to errant CEOs and controlling shareholders, may be as economically important in corporate finance as the more familiar problems of insufficient loyalty of corporate insiders to shareholders.Overt conflict between rival authorities is shown to reverse the "agentic shift" -justifying institutions that formalize argumentation such as the adversary system in Common Law courts; the Official Opposition in Westminster democracies; discussants and referees in academia; and independent directors, non-executive chairs, and proxy contests in corporate governance.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".