Bibliographic record
Abstract
Few other policy zones are as complex as the issue of climate change.If the more pessimistic projections of climate change doom are correct,then the failure to address the issue is likely to be catastrophic and irreversible.The Inter-governmental Panel on Climate Change has predicted the potential extinction of many species and that the existence of small-island and other vulnerable countries will be threatened if business-as-usual greenhouse gas emissions continue.Climate change is a transboundary problem and requires unprecedented levels of cooperation between states and serious and sustained responses from major emitters.However,the growing demand and consumption of natural resources for continued energy security and cornucopian economic growth have undermined the outcomes of international climate change negotiations.It is argued here that there is a strong connection between the major emitters’positions at United Nations’climate talks,their possessions,dependence and consumption of natural resources,and the continued undermining of international climate change policy for unsustainable growth.This paper assesses the resource politics of the US,China,India,Canada,Russia,and Saudi Arabia and their positions at climate talks to show the link between lack of climate change policy progress and the positions of these main players.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 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".