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Record W2535554686 · doi:10.1080/17470218.2016.1251472

Exceptionalist naturalism: Human agency and the causal order

2016· article· en· W2535554686 on OpenAlexafffund
John Turri

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsOntario Ministry of Economic Development and Innovation
KeywordsNaturalismMetaphysicsAgency (philosophy)EpistemologyAction (physics)Variety (cybernetics)Transcendence (philosophy)Subject (documents)AnalogyOrder (exchange)Natural (archaeology)PsychologyCognitive scienceSociologyPhilosophyComputer scienceArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

This paper addresses a fundamental question in folk metaphysics: How do we ordinarily view human agency? According to the transcendence account, we view human agency as standing outside of the causal order and imbued with exceptional powers. According to a naturalistic account, we view human agency as subject to the same physical laws as other objects and completely open to scientific investigation. According to exceptionalist naturalism, the truth lies somewhere in between: We view human agency as fitting broadly within the causal order while still being exceptional in important respects. In this paper, I report seven experiments designed to decide between these three competing theories. Across a variety of contexts and types of action, participants agreed that human agents can resist outcomes described as inevitable, guaranteed, and causally determined. Participants viewed non-human animal agents similarly, whereas they viewed computers, robots, and simple inanimate objects differently. At the same time, participants judged that human actions are caused by many things, including psychological, neurological, and social events. Overall, in folk metaphysics, human and non-human animals are viewed as exceptional parts of the natural world.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.357
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2016
Admission routes2
Has abstractyes

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