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Record W2740956760 · doi:10.1080/13688790.2017.1357219

Framing indigenous self-recognition: the visual and cultural work of the politics of recognition

2017· article· en· W2740956760 on OpenAlexaffabout
Lara Fullenwieder

Bibliographic record

VenuePostcolonial Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsQueen's University
Fundersnot available
KeywordsFraming (construction)PoliticsIndigenousSovereigntySubversionSociologyVisual cultureAestheticsMedia studiesPolitical scienceLawArtHistoryAnthropology

Abstract

fetched live from OpenAlex

Visual and cultural modes of expression and intercultural engagement have broad implications for recognition politics. Recognition-based strategies for the governance of Indigenous difference in settler colonies engage in an economy of perception that capitalises on the currency of inclusion and diversity. This paper explores the visual and cultural fields of recognition politics in the Canadian settler state through the examples of the 2008 Apology from the federal government for Indian Residential Schools and the stained-glass window – Giniigaaniimenaaning (Looking Ahead) by Métis artist Christi Belcourt – commissioned to commemorate the Apology. The paper uses Judith Butler’s concepts of recognisability and framing to make sense of these events as legitimations of settler colonialism. It goes on to explore the possibility of rupture in the inherent instability of ‘frames of recognition’, in Butler’s terms, and uses Jolene Rickard’s conceptualisation of visual sovereignty to also make sense of the simultaneous subversion and self-recognition that takes place in Belcourt’s artwork. In doing so, this paper furthers a critical dialogue surrounding the normativity of recognition policies and practices in Canada as well as the intersubjective or interpellative orientation of visual-cultural expressions of recognition.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.509
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.125
Scholarly communication0.0140.005
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.360
Teacher spread0.317 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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