Bibliographic record
Abstract
Major Strasser has been shot … Round up the usual suspects! Captain Louis Renault, played by Claude Rains, in the movie Casablanca Introduction The objective of this chapter is to sketch the contours and theoretical basis of a conception of political ethics that is expressly animated by democratic principles. The view I develop challenges the sufficiency of traditional understandings of political ethics that locate political ethics primarily in rules and norms regulating conflict of interest. Although the analysis of conflict of interest forms a legitimate and important part of political ethics, I argue that discourse about political ethics needs to be broadened and extended to encompass matters beyond issues of improper financial gain by public officials. We have reason to scrutinize the conduct not just of politicians and public servants, but also of citizens, the media, political consultants, and private businesspersons. And we have reason to include within the ambit of political ethics questions about how democratic ideals of participation, deliberation, and political equality are affected by the conduct of such actors. Although I argue for an expansion of the contours of political ethics, the position I develop is, in important respects, complementary to conflict-of-interest research. The investigation of conflict-of-interest issues that arise in political settings is ultimately motivated by a concern for the health of democratic politics. There is a natural concern to want to determine how well democratic institutions and processes are served by various conflict-of-interest norms.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".