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Record W4312944742 · doi:10.54916/rae.119309

Playing Slow

2021· article· en· W4312944742 on OpenAlexafffund
Madiha Sikander, Candice Okada

Bibliographic record

VenueResearch in Arts and Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of the Fraser Valley
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper investigates processes that include weaving, macramé, and needlework.The very nature of these practices requires devoted physical presence, firmly fixed for long hours to build rhythm.Labourious and tedious, these crafts bring one to the present by means of minute and constrained gestures.60 Research in Arts and Education | 2 / 2021 PLAYING SLOW Enduring the process at hand, the psyche is anxious and persistently apprehensive.Despite this, the work is undertaken with pleasure, entangling the practitioner such that she is unable to depart the site of labour.Touch and the repetitive working and reworking of materials allow for pleasure, enjoyment and being focused on the present moment.These lived experiences are what feminist scholar Anne Cvetkovich accredits to "the value of process and the art of daily living" through an "embodied practice."Drawing from feminist interpretations of Lacanian jouissance, this paper locates presence and attentiveness via slowness as a primary site of female creativity that differs.Both as a gesture of delaying and as being other, a play Derrida refers to as 'Différance' it is particularly generative for new ways of knowing.As a means of refinement that "work upon or shape" the practices of needlework, macramé and weaving can be understood as means of knowledge and ways of being in the 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 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)0.005
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 score0.071
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0710.014

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 head0.322
GPT teacher head0.470
Teacher spread0.148 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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