Bibliographic record
Abstract
This is a moral theory of roles. It asks how the special rules and purposes of institutions (such as legal systems, corporations, and governments) can change the moral rights and responsibilities of role occupants (such as lawyers, managers, and politicians). Its answer, the filtering account, is that legitimate institutions selectively filter the considerations that a role occupant may or must give weight to in doing her job. On this view, to perform a role with integrity is to respect the scope of the role’s filters, sifting the reasons for action that must be excluded from those that must remain on the moral scales. The filtering account is distinguished from two alternatives in the literature: the view that roles simply add additional reasons to the balance, and the view that roles entirely shield their occupants from ordinary moral criticism. The account is further explained in terms of a distinction between first-order and second-order reasons, which is then applied to a colourful range of examples—featuring cynical corporate lobbyists and conflicted criminal lawyers, unflinching public regulators and buck-passing middle managers. The result is a system for understanding the moral structure of roles, including the bounds of professional discretion, the ethics of official disobedience, and the relationship between individual and institutional integrity. The view is presented as a liberal theory in its respect for the moral claims of legitimate roles, as a critical theory in its diagnosis of the tendency for those same claims to be stretched and distorted.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".