Bibliographic record
Abstract
Abstract This book argues that Britain owes reparations to the Caribbean. The case depends on an historical and a moral claim. The historical claim is that the exploitation of generations of the islands’ inhabitants under slavery and colonialism wronged those people while enriching Britain—and crucially that, as we are the inheritors of these riches, so present generations in the Caribbean are inheritors of relative poverty. The moral claim is that where such a moral wrong has been done, moral repair or reparations are due. Racism was the enabling doctrine for slavery and colonialism, and racism is a part of its history which Britain has failed to confront. There are many familiar objections to the very idea of making reparations: ‘It was a long time ago’, ‘slavery was legal back then’, etc. There are also practical objections, especially to any monetary payment which may form part of reparations—who will pay what to whom? Taking as a starting point the £20m which the British government paid to slave owners at abolition, the book proposes a provisional sum as the starting point for negotiations with the Caribbean nations. And while the final settlement must be a matter between national governments, it is argued that other institutions, such as universities and churches, can advance the case for reparations by themselves understanding and addressing their own entanglements with enslavement and its enduring legacies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".