Intentional action and knowledge-centered theories of control
Bibliographic record
Abstract
Abstract Intentional action is, in some sense, non-accidental, and one common way action theorists have attempted to explain this is with reference to control. The idea, in short, is that intentional action implicates control, and control precludes accidentality. But in virtue of what, exactly, would exercising control over an action suffice to make it non-accidental in whatever sense is required for the action to be intentional? One interesting and prima facie plausible idea that we wish to explore in this paper is that control is non-accidental in virtue of requiring knowledge—either knowledge-that or knowledge-how (e.g., Beddor and Pavese 2021; cf., Setiya 2008; 2012 and Habgood-Coote 2018). We review in detail some key recent work defending such knowledge-centric theories of control, and we show that none of these accounts holds water. We conclude with some discussion about how control opposes the sort of luck intentional action excludes without doing so by requiring knowledge (that- or how).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".