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Record W4392649078 · doi:10.1086/729335

Reactivation and Remediation through Artistic Intervention

2023· article· en· W4392649078 on OpenAlex
Lelland Reed, Ali Dixon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueArt Documentation Journal of the Art Libraries Society of North America · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsNSCAD UniversityUniversity of Calgary
Fundersnot available
KeywordsEnvironmental remediationIntervention (counseling)PsychologyPsychiatryContaminationBiologyEcology

Abstract

fetched live from OpenAlex

Unedited and raw time-based media—dailies, rushes, scratches, original or source footage, B-roll, and more—go by different names depending on the context of creation. This article examines the collecting, preservation, and access practices of raw, unedited, and auxiliary media through the lens of initial findings of a scoping review underway by the authors. Exploring the literature and incorporating post-custodial and media archeology frameworks expanded the authors’ research design to embrace the potential of access, reuse, and reactivation of materials as means of remediation. This paper discusses the potential raw, unedited, and auxiliary media pose for galleries, libraries, archives, and museums, as well as broader communities, and it encourages readers to embrace relationships with raw and unedited media, reuse, and resistance by way of community and artistic intervention. [This article is an expansion of a presentation given at the International Association of Sound and Audiovisual Archives (IASA) conference, held virtually in September 2021.]

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.218
GPT teacher head0.507
Teacher spread0.289 · 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