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Record W4384616469 · doi:10.3998/ergo.3590

Intentional Action, Know-how, and Lucky Success

2023· article· en· W4384616469 on OpenAlexafffund
Michael Kirley

Bibliographic record

VenueErgo an Open Access Journal of Philosophy · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsConflationEpistemologyJudgementAction (physics)Tacit knowledgePsychologyPhilosophySociology

Abstract

fetched live from OpenAlex

Elizabeth Anscombe held that acting intentionally entails knowing (in a distinctively practical way) what one is doing. The consensus for many years was that this knowledge thesis faces decisive counterexamples, the most famous being Donald Davidson’s carbon copier case, and so should be rejected or at least significantly weakened. Recently, however, a new defense of the knowledge thesis has emerged: provided one understands the knowledge in question as a form of progressive judgement, cases like Davidson’s pose no threat. In this paper, I argue that this neo-Anscombean maneuver fails because it is founded on an untenable conception of the difference between intentional and merely lucky success. More specifically, the neo-Anscombean view conflates merely lucky success with subjectively surprising success. Unlike the former, subjectively surprising success may well be intentional, for it may well be the result of an exercise of knowledge-how. After sketching an alternative view that better captures the intuitive contrast between lucky and intentional success, I argue that the conflation of surprising and merely lucky success owes to a tacit commitment to the thesis that knowing how entails knowing that one knows how. This thesis is not only false, but distortive of the explanatory role of knowledge-how. This result, in turn, tells us something important about what practical knowledge cannot be.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.268
GPT teacher head0.420
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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