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Pleasure, Displeasure, and Representation

2001· article· en· W385662602 on OpenAlexaff
Timothy Schroeder

Bibliographic record

VenueCanadian Journal of Philosophy · 2001
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPleasureObject (grammar)Philosophy of mindPain and pleasureEpistemologyRepresentation (politics)PhilosophySkepticismNeglectDirect and indirect realismPsychologyMetaphysicsRealismLawNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

Pleasure and displeasure have been suffering from intellectual neglect in the philosophy of mind. In contemporary work, the mode of experience which effectively dominates discussion is vision—David Marr's work on visual representation and L. Weiskrantz's work on blindsight are familiar to many philosophers of mind, as are the philosophical uses of such work, and no one seems to tire of working out what the frog's eye tells the frog's brain. Who, though, can name a leading theorist of pain? As a source of examples and intuitions, pain is a perennial favorite in ethics and the philosophy of mind, but in both disciplines pain is taken for granted far more often than it is the object of analysis. Equally significantly, forms of displeasure other than pains are very largely neglected. Pleasure, for its part, has been the nigh-exclusive province of moral theorists; few other than Strawson seem to have taken a special interest in it in the philosophy of mind. The object of the present work is to rectify this neglect, and to give an account of pleasure and displeasure which reveals a striking degree of unity and theoretical tractability underlying the diverse phenomena: arepresentationalistaccount.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.059
Scholarly communication0.0100.015
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.170
GPT teacher head0.288
Teacher spread0.117 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2001
Admission routes1
Has abstractyes

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