Toxic colonialism: Between sickness and sanctuary on Ilet la Mère, French Guiana
Bibliographic record
Abstract
Since the establishment of slave plantations in French Guiana during the 17th century, the small island of Ilet la Mère, located 11 km from Cayenne, has functioned as site of confinement, refuge, and experimentation. These roles continued during and after the creation of France’s largest penal colony across the territory (1852–1953). This article identifies different phases of Ilet la Mère’s colonial and postcolonial histories, and argues that the island plays an integral role in the ongoing perception and administration of French Guiana as colonial outpost and underexploited natural resource. This involves frequent misconceptions that French Guiana itself is an island, and the metonymic evocation of its islands, notably Devil’s Island (one of the Salvation Islands) and Cayenne, to denote the entire territory. Such perceptions, applied from outside the territory, alongside local engagement with lesser-known islands like Ilet la Mère, contribute to the creation of a ‘toxic island ecology’. Toxicity, defined as more than contagion and contamination, incorporates other practices and discourses which work to draw attention away from environmental and human rights abuses taking place on the mainland. The article concludes with reflection on the island’s current usage as a nature sanctuary where visitors can interact with overly tame squirrel monkeys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".