Bibliographic record
Abstract
This paper presents a conception of corruption informed by epistemic democratic theory. I first explain the view of corruption as a disease of the political body. Following this view, we have to consider the type of actions that debase a political entity of its constitutive principal in order to assess corruption. Accordingly, we need to consider what the constitutive principle of democracy is. This is the task I undertake in the second section where I explicate democratic legitimacy. I present democracy as a procedure of social inquiry about what ought to be done that includes epistemic and practical considerations. In the third section, I argue that the problem of corruption for a procedural conception of democracy is that the epistemic value of the procedure is diminished by corrupted agents’ lack of concern for truth. Corruption, according to this view, consists in two deformities of truth: lying and bullshit. These deformities corrupt since they conceal private interests under the guise of a concern for truth. In the fourth section, I discuss the difficulties a procedural account may face in formulating solutions to the problem of corruption.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".