The case for ecological reparations in Africa
Bibliographic record
Abstract
The COP 27 in Sharm El-Sheikh made the point: the world faces a novel problem. The scale of socio-ecological crises that afflict the earth is unprecedented. According to the latest assessment by the IPCC, these problems are worsening and will continue to do so. There is more than 50% chance that global warming will reach or exceed 1.5°C in the near-term (Intergovernmental Panel on Climate Change [IPCC], 2022). The ramifications are certain, but uneven (IPCC, 2022, p.14). Reversing rapid biodiversity loss has also eluded humanity since the first global agreement to do so by 2010.[1] So, the forthcoming COP 15 in Montreal, Canada, will revisit the issue. This attempt to revisit the 1992 Earth Summit in Rio, ratified by every UN member state except the U.S, is critical for Africa. Whether in terms of climate change or biodiversity loss, COP 27 or COP 15, the regions of highest exposure are Africa and elsewhere in the Global South (IPCC, 2022, p. 14). Not only 3.6 billion people face existential outcomes, but also many plants and animals risk total extinction (IPCC, 2022, pp. 14-16). In his book, Extinction, Ashley Dawson (2016, pp. 7-8) points out that in the last 20 years, 70,000 African elephants have been killed and the number of rare forest elephants in Africa has declined by 60%; We are all at risk of extinction. This is an emergency.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 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".