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Record W2593331592 · doi:10.3366/film.2017.0029

‘Do I feel lucky?’: Moral Luck, Bluffing and the Ethics of Eastwood's Outlaw-Lawman in<i>Coogan's Bluff</i>and the Dirty Harry Films

2017· article· en· W2593331592 on OpenAlexaff
Joel Deshaye

Bibliographic record

VenueFilm-Philosophy · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLuckBluffCharacter (mathematics)VirtueEconomic JusticeRecklessnessPsychoanalysisPhilosophyLawSociologyPsychologyEpistemologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

In Coogan's Bluff (1968) and the Dirty Harry films, Clint Eastwood's characters often invoke luck when they want unpredictable others to assume some responsibility to stop violence, thereby implicating moral luck in heroism. In the famous ‘Do I feel lucky’ scene from Dirty Harry (1971), Eastwood's character might not be bluffing, but he is giving luck a role in justice. In this case and others, his character's unconventional responsibility should prompt reconsideration of his character's virtue. Viewers must also decide where the deceptive or rule-breaking policeman locates the responsibility for his actions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.283
Teacher spread0.217 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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