Bibliographic record
Abstract
There is an international consensus that climate change is caused by human activities which substantially increase the atmospheric concentration of greenhouse gases.The ill-effects of climate change are droughts which adversely affect the global poor who are engaged in agriculture; storm surges which destroy local infrastructure, housing and crops; and the rise of sea levels which adversely affect the inhabitants of small island states which could eventually be totally submerged. Military strategists and intelligence analysts are preparing for future conflicts likely to be caused by environmental security issues.The objects of this article are: (i) to evaluate the ill-effects of climate change as a matter of global justice, (ii) to consider whether future generations have the right not to suffer from the ill-effects of climate change, and if so, (iii) to evaluate the relevant conceptions of global justice, and (iv) to assess critically whether international law provides effective preventive responses to climate change and environmental security threats.Finally, a monist-naturalist conception of global justice privileging human dignity as one of its guiding principles is proffered as a solution to the problems raised by the mechanisms of dealing with the ill-effects of climate change and the attendant environmental security issues under international law.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".