Bibliographic record
Abstract
When it comes to European descriptions of the Other, the act of cannibalism has long been synonymous, to the Western imagination at least, with primitivism. As such, it often operates as a boundary line between the savage and the civilised. The spectre of the Kanak “anthropophages” of New Caledonia and the New Hebrides (Vanuatu) is ever-present in the writings of Georges Baudoux, whose fascination with cannibalism reflects the 19th-century colonial preoccupation with racial hierarchies and the demonization of the indigenous Other. Indeed, in his Légendes canaques, Baudoux’s representations of Kanak cannibalism are typical of the colonial literary genre – overly bloodthirsty, sensationalised and designed to distance the “instictive savages” or “cannibal animals” from the “rational” (read “superior”) colonizers. While Baudoux does not abandon this discourse in other stories, it is interesting to see how it is nuanced in the case of the métis (Kanak-European) protagonist of Jean M’Baraï. This paper explores the representations of the Other eating, including eating the Other, in Baudoux’s work, focusing particularly on the actions/reactions/reflexivity of Jean M’Baraï. To what extent can we see this character as a vehicle for conflicting colonial discourses on the métis as either “deviant degenerate” or the “great hope” for the future “civilization” of the colonized “race”? And where does Baudoux place himself in this clash of ideologies?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".