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Record W3135544387 · doi:10.36939/cjur/vol29no1/art272

Invitations from the land and waters: Lessons from the Peace of Fort Garry

2020· article· en· W3135544387 on OpenAlexaffvenueabout
Daniel Voth

Bibliographic record

VenueCanadian journal of urban research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIndigenousMetisPoliticsScholarshipGeographyPopulationPolitical scienceEnvironmental ethicsSociologyPublic administrationLawEcology

Abstract

fetched live from OpenAlex

This paper offers critical perspectives on engaging Indigenous Peoples land-based practices in the city. Using Winnipeg as its case study, this research identifies Winnipeg and the Red River area as a major pre-settler Indigenous population centre. Through an examination of the making of a peace treaty between the Great Sioux Nation, the Métis, and the Saulteaux at Fort Garry as a moment for exploring the land and waters as sentient entities inviting Indigenous Peoples to gather and engage in political activities. This work provides insights into the way the land and waters convey invitations to other beings, and explores what political values and activities those invitations have the power to encourage. What follows contributes to the ongoing scholarship of reclaiming urban geographies as Indigenous spaces, and challenging the reserve-rural-remote world as an Indigenous space, and the urban as a non-Indigenous space. By thinking about Indigenous politics in the city through the framework of what the land and waters invite, readers will be opened to the potential to transform contemporary inter-Indigenous political and cultural activity in places like Winnipeg.

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)0.007
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0500.043
Scholarly communication0.0150.008
Open science0.0030.014
Research integrity0.0040.007
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 head0.131
GPT teacher head0.385
Teacher spread0.254 · 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

Citations2
Published2020
Admission routes3
Has abstractyes

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