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Record W4375815336 · doi:10.1386/ijfs_00051_1

Unsettling ‘we’re all mixed-race’: Métis.se/colonial futurity, settler colonialism and the countering of Kanak sovereignty

2022· article· en· W4375815336 on OpenAlexaboutno aff
Anaïs Duong-Pedica

Bibliographic record

VenueInternational Journal of Francophone Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIndigenousGender studiesSovereigntyPoliticsIndependence (probability theory)Context (archaeology)SociologyPolitical scienceMedia studiesHistoryLaw

Abstract

fetched live from OpenAlex

This article aims to uncover the colonial character of the statement ‘on est tous métis’ (‘we’re all mixed-race’) in Kanaky/New Caledonia, a racially and politically polarized space where there is an ongoing struggle for independence led by Indigenous Kanak people. It uses data gathered in semi-structured interviews with self-identified ‘mixed-raced’ people during a six-month stay in Kanaky/New Caledonia before and after the November 2018 referendum for independence. It also uses ethnographic material and, more specifically, encounters with the figure of the ‘mixed-race’ person in political debates, campaigns as well as art and media that signal an investment in the idea that, in Kanaky/New Caledonia, ‘we are all mixed-race’. The article exposes the political discourse of multiracialism as exclusionary and as a mechanism of Indigenous disappearance in the settler colonial context. It also sheds light on the way in which settler anxiety feeds the multiracial discourse. In challenging and deconstructing the orientations towards a multiracial or métis.se future, that individuals and institutions imagine, wish or advocate for, the article aims to call for a desolidarization from modes of thinking and being that support the French colonial project, even when it masks itself as inclusive.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.332
Teacher spread0.302 · 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 teacher head, 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

Citations5
Published2022
Admission routes1
Has abstractyes

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