Bibliographic record
Abstract
Abstract: In this essay I argue that adversarial institutional systems, such as multi-party democracy, present a distinctive risk of institutional corruption, one that is particularly difficult to counteract. Institutional corruption often results not from individual malfeasance, but from perverse incentives that make it the case that agents within an institutional framework have rival institutional interests that risk pitting individual advantage against the functioning of the institution in question. Sometimes, these perverse incentives are only contingently related to the central animating logic of an institution. In these cases, immunizing institutions from the risk of corruption is not a theoretically difficult exercise. In other cases, institutions generate perverse or rival incentives in virtue of some central feature of the institution’s design, one that is also responsible for some of the institution’s more positive traits. In multi-party democratic systems, partisanship risks giving rise to too close an identification of the partisan’s interest with that of the party, to the detriment of the democratic system as a whole. But partisanship is also necessary to the functioning of such a system. Creating bulwarks that allow the positive aspects of partisanship to manifest themselves, while offsetting the aspects of partisanship through which individual advantage of democratic agents is linked too closely to party success, is a central task for the theory and practice of the institutional design of democracy.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".