Looking to the Horizon: The Meanings of Reparations for Unbearable Crises
Bibliographic record
Abstract
Harms that arise from climate catastrophes deepen already unbearable forms of racial oppression. Both can be traced to accumulative ways of life that justified slavery and colonialism, which shifted into new forms of hegemony under liberal international law. A growing response has been to demand reparations. However, the meanings of reparations are vast and sometimes counterintuitive. This essay reflects on reparations claims emanating from the Caribbean, as one place where race and ecology converge. The Caribbean was forged by Indigenous genocide, the enslavement of African peoples, and the indentured labor of Asian peoples. Today, descendants in the region face subordination under liberal international law and climate catastrophes. Such conditions reveal that reparations are foremost a horizon of transformation away from accumulative ways of life that spread from Europe to the world, structuring the present reality. “Reparations” also refers to immediate justices that meet the demands of those who are harmed, because this prefigures the horizon of transformation by disrupting imperialism. These qualities dispel racializing critiques from the First World that reparations are irrational, or constitute politics separate from law. Reparations can enact legal relations that are meaningful to those “on the bottom,” and emancipatory for everyone, when communities and social movements define them.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.078 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".