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Record W3006409130 · doi:10.1177/1750698019900953

Making memory sovereign/making sovereign memory

2020· article· en· W3006409130 on OpenAlexafffund
May Chazan, Jenn Cole

Bibliographic record

VenueMemory Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMemory workIndigenousColonialismCollective memoryNarrativeStorytellingSovereigntyReading (process)Cultural memorySociologyMedia studiesHistoryEpistemologyAnthropologyLiteraturePoliticsPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

This article foregrounds the activist memory projects of four Indigenous women artists, recorded as part of a digital storytelling project in 2018. These memory projects collectively represent a refusal of settler colonial frameworks and a grounding in Indigenous knowledges, which challenge institutional understandings of the archive and dominant conceptions of memory. Through close reading and analysis, we argue that these storytellers’ practices – rooted in Land, body, ancestral relations, and creativity – are not efforts to simply right the colonial archive, nor are they insertions into colonial narratives; instead, they remember differently, with distinct modes and mechanisms for accessing, producing and circulating memory. Their work, in concert with Indigenous scholars cited throughout this article, extends not only the epistemological basis of the archive. It also expands the ontology of memory: pushing memory scholars to expand their understandings of what is possible to remember, and how memory is accessed and shared.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.013
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.040
Scholarly communication0.0130.022
Open science0.0010.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.326
GPT teacher head0.313
Teacher spread0.012 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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