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. \n \nWhether 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".