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Record W2983604235 · doi:10.1177/1077800419884964

Socially Engaged Art, Experimental Pedagogies, and Anarchiving as Research-Creation

2019· article· en· W2983604235 on OpenAlexafffund
Stephanie Springgay, Anise Truman, Sara MacLean

Bibliographic record

VenueQualitative Inquiry · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAccountabilitySociologyReciprocity (cultural anthropology)HegemonyStewardship (theology)ColonialismEconomic JusticePower (physics)AestheticsEnvironmental ethicsMedia studiesLawPolitical scienceSocial sciencePoliticsArt

Abstract

fetched live from OpenAlex

Archives, as repositories of culture and knowledge, are closely linked to colonial power, control, hegemony, and conquest. In recognizing the limitations and problems of conventional archives, scholars and artists offer counter-archiving as a method of interrogating what constitutes an archive and the selective practices that continuously erase particular subjects. Unlike static, stable, and linear colonial archives, counter-archives are grounded in accountability and reciprocity. Similarly, the anarchive is concerned with what it can do in the present-future. As such, anarchiving is less a thing, then a process or an action. This article examines anarchiving as research-creation practices through three provocations: anarchiving as indeterminate transformation, anarchiving as felt, and anarchiving as response-ability. We examine a particular anarchiving project Instant Class Kit dedicated to radical pedagogies and social justice. Anarchiving is fundamentally about practicing an ethics based on response-ability, stewardship, care, and reciprocity that center relationships to land, territory, human, and more-than-human bodies.

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)0.020
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.077
Scholarly communication0.0100.009
Open science0.0020.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.296
GPT teacher head0.445
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 designQualitative
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

Citations27
Published2019
Admission routes2
Has abstractyes

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