Unsettling ‘we’re all mixed-race’: Métis.se/colonial futurity, settler colonialism and the countering of Kanak sovereignty
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".