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Record W2335787882 · doi:10.1080/14649365.2016.1164230

‘Thinking of the land <i>in that way</i> ’: indigenous sovereignty and the spatial politics of attentiveness at Skwelkwek’welt

2016· article· en· W2335787882 on OpenAlexafffund
Sean Robertson

Bibliographic record

VenueSocial & Cultural Geography · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Alberta
FundersSimon Fraser UniversitySocial Sciences and Humanities Research Council of CanadaLaw Foundation of British Columbia
KeywordsIndigenousPoliticsSovereigntySociologyGrassrootsEnvironmental ethicsEpistemologyIdentity (music)FeelingMateriality (auditing)AestheticsPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Grassroots Secwepemc attempted to stop the expansion of Sun Peaks resort on a site for Indigenous knowledge. I attend to the practice of Indigenous knowledge related to the confrontation and assert that Indigenous sovereignty and identity were outcomes. To make this argument, I investigate the space made for emotion, affect and intuition in the performance of Indigenous knowledge. From a relational materialist position, these more-than-representational forces play an important role in epistemology, ontology, ethics, and subjectivity. Identity therefore materialized in three ways related to feeling(s). As a result of their attunement to these forces, the Secwepemc understood that (non)humans underpin their material being and placed the collective on the political horizon. Furthermore, the practice of Indigenous knowledge encouraged an identity characterized by “attentiveness” to feeling(s) and to the activity of feeling itself. From the perspective of the struggles of subjugated knowledge, I also contextualize the attentiveness at the core of Indigenous knowledge practices as part of a more-than-representational and decolonizing spatial politics.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations25
Published2016
Admission routes2
Has abstractyes

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