Bibliographic record
Abstract
In what sense is intentional action a form of intelligent activity? The aim of this dissertation is to motivate and defend a cognitive conception of intentional action, on which intentional action is a distinctive form of cognitive success. In Chapter 1, I examine recent efforts to defend a version of the cognitivist thesis against influential apparent counterexamples. These efforts fail, I argue, because they misunderstand the difference between intentional and merely lucky success. More specifically, they conflate merely lucky success with subjectively surprising success, and consequently distort the explanatory role of knowledge-how.In Chapter 2, I identify the Trying-First approach—on which intentional action is just a certain kind of successful trying—as the fundamental obstacle to motivating a cognitive conception of intentional action. But, I argue, any coherent conception of trying must presuppose a notion of knowledge-how: a trying is either an exercise of knowledge-how or it is a defective version of such. Since the explanatory role of knowledge-how is to account for the non-luckiness of success characteristic of intentional action, it follows that one cannot analyze intentional action in terms of trying. In Chapter 3, I begin to develop the positive view, arguing that intentional action is identical to the manifestation of knowledge-how. Defending this identity thesis against what initially seem to be powerful counterexamples reveals that manifestations of knowledge-how are partly constituted by the successful exercise of cognitive abilities. This result, in combination with those of the previous chapters, implies that intentional action has conceptual priority over other forms of intelligent activity. In Chapter 4, I argue that these results motivate a cognitive conception of intentional action. The cognitive constituents of a given manifestation of knowledge-how may comprise a heterogeneous collection of cognitive activities. Nevertheless, these cognitive activities are unified: their deliverances are integrated into a single idea, which idea constitutes the agent’s grasp of what she is doing. When the agent acts intentionally, that she all along grasps what she is doing amounts to a distinctive form of cognitive success. This is what I call practical knowledge.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.053 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".