Bibliographic record
Abstract
Abstract In this book Shepherd offers a perspective on the shape of agency by offering interlinked explanations of the basic building blocks of agency, as well as its exemplary instances. In the book’s first part, he offers accounts of phenomena that have long troubled philosophers of action: control over behavior, non-deviant causation, and intentional action. These accounts build on earlier work in the causalist tradition and undermine the claims of many that causalism cannot offer a satisfying account of non-deviant causation, and therefore intentional action. In the book’s second part, he turns to modes of agentive excellence—ways that agents display quality of form. He offers a novel account of skill, including an account of the ways that agents display more or less skill. He discusses the role of knowledge in skill and concludes that while knowledge is often important, it is inessential. This leads to a discussion of knowledge of action—of the way that knowledge of action and knowledge of how to act informs action execution. Shepherd argues that knowledgeable action includes a unique epistemic underpinning. For in knowledgeable action, the agent has authoritative knowledge of what she is doing and how she is doing it when and because she is poised to control her action by way of practical reasoning.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".