Reparations For Climate Harm and The Role of The Loss and Damage Mechanism: Lessons from Other Areas of Law
Bibliographic record
Abstract
The Global South is increasingly calling upon nations in the Global North to develop finance mechanisms to combat loss and damage. However, loss and damage remains underdeveloped due to the Global North’s current refusal to accept financial responsibility for climate harm occurring in vulnerable nations. Scholars have argued that the finance for loss and damage should be based upon the principle of reparations, but there is very limited research exploring how a reparations scheme should work. This article explores two existing reparations schemes: Canada’s Truth and Reconciliation Commission and Australia’s National Redress scheme, to identify successful elements and limitations of these schemes. Based on this analysis, this article proposes that a climate reparations scheme should be informed by Indigenous voices and knowledge and focus on providing multiple forms of meaningful redress. The paper also finds that it is possible to develop a reparations scheme without necessarily imposing liability on the Global North. It argues that a scheme of this nature may result in the Global North being more willing to develop financing to combat loss and damage and start paving the way for meaningful climate reparations.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".