Bibliographic record
Abstract
This essay differentiates two approaches to understanding the concept of coercion, and argues for the relative merits of the one currently out of fashion. The approach currently dominant in the philosophical literature treats threats as essential to coercion, and understands coercion in terms of the way threats alter the costs and benefits of an agent’s actions; I call this the “pressure” approach. It has largely superseded the “enforcement approach,” which focuses on the powers and actions of the coercer rather than the perspective of the coercee. The enforcement approach identifies coercion with certain uses of the kinds of powers that agents need to accumulate and wield in order to be able to make significant, credible threats. Though there is considerable overlap extensionally in the instances of coercion recognized by the two approaches, the enforcement approach encompasses some uses of power to coerce that do not involve threats (in particular some direct uses of physical force). It also circumscribes which threats should be counted as coercive, though notably it provides a picture of coercion that is non-moralized in its essentials. While there may be specific purposes for which a pressure account is to be preferred, I argue that the enforcement approach better describes how coercion works, and elucidates factors that are often tacitly assumed by pressure accounts. It also is more useful for explaining the social and political significance of coercion, and why coercion is thought to have the implications commonly associated with it. In particular, I argue that it helps us understand why uses of coercion are in general a matter of ethical significance, why state authority depends on commanding a monopoly on the right to use coercion, and why being coerced may reasonably provide one a defense against being held responsible for actions one is coerced into taking.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".