Holding Polluting Countries to Account for Climate Change: Is “Loss and Damage” Up to the Task?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Formally established by the Conference of the Parties to the UN Framework Convention on Climate Change in 2013, the Loss and Damage Mechanism represents what is for many an important effort on the part of developing countries (including China and the G77) to hold polluting countries to account for past and potential harms incurred as a result of climate change. This paper explores the viability of using the Mechanism as a means of holding polluting countries to account for the provisions outlined in the Framework Convention. In reviewing the history and recent policy within the UNFCCC, the paper makes the case that demands for greater accountability through the Loss and Damage Mechanism have been frustrated by a lack of consensus about the rights of poor countries to pursue carbon‐intensive development pathways, the obligations of current and future generations to the actions and decisions of their forebears, and the obligations of national governments to their own citizens and the UNFCCC. Instead of assigning responsibility for past and future losses and damages, the Mechanism has gravitated toward a more technocratic/bureaucratic exercise aimed at collecting data, enhancing knowledge, and making policy recommendations.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it