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Record W2394937533

Waasaabikizo: Our pictures are good medicine

2016· article· en· W2394937533 on OpenAlexaffabout
Celeste Pedri-Spade

Bibliographic record

VenueDecolonization: Indigeneity, Education & Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDecolonizationPrivilege (computing)IndigenousNarrativePhotographyIdentity (music)Context (archaeology)SociologyGender studiesAestheticsVisual artsHistoryPolitical scienceLawArtArchaeologyLiteraturePolitics
DOInot available

Abstract

fetched live from OpenAlex

This article explores the role of Anishinabe photography in the ongoing struggle to decolonize, among Anishinabeg with ancestral and present day relationships to lands now occupied predominantly by settler peoples in northwestern Ontario. Drawing on work carried out with several families, this article connects the collection and experience of photography to decolonization, emphasizing its processual nature and role in mediating memories of the past in ways that are respectful of, and privilege, Anishinabe culture and knowledge. By contextualizing this work within a context of Indigenous photography and decolonization, this article furthers understandings of the significance of Indigenous photography to Indigenous led efforts directed towards reclaiming identity, cultural memory, intergenerational knowledge, and sovereignty. This work reveals how Anishinabe photography privileges Anishinabe narratives and experiences that, in turn, counter dominant versions of history and operate as a powerful decolonial force. Overall findings of this research reveal methodological and applied understandings of how photography contributes to ongoing Anishinabe efforts towards decolonization.

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)0.000
metaresearch head score (Gemma)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.001

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 head0.325
GPT teacher head0.588
Teacher spread0.264 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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