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Record W1023462183

Judging the Judges: Wittgenstein’s Sceptical Paradox for Debates in the Philosophy of Neuroscience

2008· article· en· W1023462183 on OpenAlexaff
Will Robbins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicPhilosophy and Theoretical Science
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHackerSkepticismRebuttalEpistemologyPhilosophyArgument (complex analysis)Reading (process)Context (archaeology)Philosophy of scienceCriticismComputer scienceLawLinguistics
DOInot available

Abstract

fetched live from OpenAlex

In their work, Philosophical Foundations of Neuroscience, Maxwell Bennett and Peter Hacker argue that modern neuroscientists are labouring under a conceptual confusion— that of wrongly ascribing psychological predicates such as “thinks” or “infers” to parts of the brain, instead of to the whole person. They contend that such use of these folk psychology concepts violates the rules of their use. This argument has met with stiff rebuttals from philosophers such as Daniel Dennett and Paul Churchland, who counter that Bennett and Hacker have overstepped the bounds of their ordinary-language analysis in the case of neuroscientific research. These defenders of the current neuroscience project argue that there are no explicit rules available to point out this supposed violation, and until such rules are made available (and shown to be valid), critics such as Bennett and Hacker have no available justification for such claims against the coherence of neuroscientist’s language use. Within the context of this debate, an examination of Ludwig Wittgenstein’s arguments on the subject of language use and rule following will be undertaken, specifically from the perspective of Saul Kripke’s reading of the Philosophical Investigations as the formulation of a unique Wittgensteinian sceptical paradox. Kripke’s reading of Wittgenstein regards numerical privacy and justification is used to provide a possible rebuttal for Bennett and Hacker to their critics in this debate.

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 categoriesnone
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 score0.406
Threshold uncertainty score0.694

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.089
GPT teacher head0.328
Teacher spread0.240 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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