MétaCan
Menu
Back to cohort
Record W4200085544 · doi:10.1080/01436597.2021.2006054

Agonistic reconciliation: inclusion, decolonisation and the need for radical innovation

2021· article· en· W4200085544 on OpenAlexaboutno aff
Sarah Maddison

Bibliographic record

VenueThird World Quarterly · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsDecolonizationIndigenousAgonistic behaviourInclusion (mineral)SovereigntyLegitimacyColonialismPolitical sciencePolitical economySociologyDemocracyLawGender studiesSocial psychologyPoliticsPsychologyEcology

Abstract

fetched live from OpenAlex

In settler colonial societies like Australia, Canada, New Zealand and the United States, Indigenous–state relations are defined by ongoing conflict over unresolved questions of sovereignty, self-determination, and land. These conflicts have remained intractable regardless of the policy approaches engineered by the state. This article outlines an analytical approach to agonistic reconciliation by mapping the polar ends of a spectrum of responses to conflict in Indigenous–settler relations, with inclusion at one end of this spectrum and decolonisation at the other. Agonistic inclusion seeks to engage reconciliatory relations within colonial democratic institutions, while agonistic decolonisation rejects the legitimacy of these institutions and seeks radical innovation in their place. The article concludes by arguing that scholars must be alert to the seductive pull of inclusion and push instead towards the radical innovation that decolonisation demands.

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)0.026
metaresearch head score (Gemma)0.020
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.117
Scholarly communication0.0140.016
Open science0.0020.021
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.305
Teacher spread0.287 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThird World QuarterlySame topicIndigenous Health, Education, and RightsFrench-language works237,207