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Record W2980991228 · doi:10.1215/00382876-7825713

How to Buy a Coffee in a Settler State:

2019· article· en· W2980991228 on OpenAlexaboutno aff
Shady Hafez

Bibliographic record

VenueSouth Atlantic Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismDecolonizationState (computer science)Capital (architecture)EthnologyGender studiesHistoryPolitical scienceSociologyLawAncient historyPolitics

Abstract

fetched live from OpenAlex

Kitigan Zibi Anishinabeg is an Algonquin community located roughly one and a half hours from Canada’s capital, Ottawa. It is a primarily English-speaking community located in the French-speaking province of Quebec. This essay will explore the complexities and difficulties associated with being an English-speaking Indigenous community that resides within the boundaries of French-speaking Quebec. In exploring this topic, this essay will uncover the often unexplored realities of Indigenous communities that are caught between the competing histories of colonial empires such as France and Britain and how those past colonial conflicts, and, in turn, the imposition of linguistic dominations, inform current difficulties around decolonization, nationhood, and Indigenous liberation.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.591
Threshold uncertainty score0.813

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0300.008
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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