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Record W4408240024 · doi:10.1017/s1752971925000016

Negotiating racial subjection: analysing Black and Indigenous resistance from within colonial orders

2025· article· en· W4408240024 on OpenAlexaff
Owen R. Brown, Arturo Chang

Bibliographic record

VenueInternational Theory · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousNegotiationColonialismResistance (ecology)Political scienceGender studiesSociologyLaw

Abstract

fetched live from OpenAlex

Abstract This article addresses recent work on empire and colonisation which calls for a reappraisal of how agency and resistance manifests among groups responding to structural marginalisation. We argue that approaching these questions from within the colonial order reveals important idiosyncrasies regarding how groups understood resistance, agency, and popular organising as possible responses that emerged from within imperial landscapes. Using the example of race as a central regulatory category and practice of colonial power, we analyse two cases which we suggest benefit from an account of agency and resistance within colonial order: the Black Loyalists in English America and the Indigenous royalists of New Granada, two groups which pursued emancipation by choosing to remain under colonial rule. The resulting analysis produces a more dynamic account of resistance and emancipation which responds to the far-reaching influence of colonial order for resistance movements at local, national, and international levels. This account contributes to recent debates which call for theoretical analysis of “middle actors” and popular thinking as it relates to international politics, postcolonial movements, and studies of empire.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.016
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.296
Teacher spread0.289 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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