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Record W2801644770 · doi:10.1177/1357034x18766285

The Skinscape

2018· article· en· W2801644770 on OpenAlexaff
David Howes

Bibliographic record

VenueBody & Society · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsPerformative utteranceSensory systemPerspective (graphical)PerceptionAestheticsObject (grammar)PsychologyVisitor patternCognitionSociologyCognitive scienceCommunicationCognitive psychologyVisual artsArtComputer scienceNeurosciencePhilosophyLinguistics

Abstract

fetched live from OpenAlex

This article theorizes the dermalogical turn – heralded by the publication of this special issue – from a sensory studies perspective. Sensory studies involves a cultural approach to the study of the senses and a sensory approach to the study of culture. The skin is both an object and means of perception. Understandings of the skin and of touch vary across cultures: the skin may be seen as social rather than individual, as porous instead of an envelope, and as knowledgeable or sentient in its own right rather than subservient to the eye or brain (i.e. cognition). These contrasting understandings have important implications for practice. Haptic Field, a performative sensory environment designed by Chris Salter, which enables the visitor to try on a second skin, is discussed as a means of shaking up conventional Western understandings of the skin and of touch, and facilitating the communication of skin knowledge across cultures.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0100.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0470.006

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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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 designNot applicable
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

Citations53
Published2018
Admission routes1
Has abstractyes

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