Bibliographic record
Abstract
Abbreviations Introduction Part I: Questioning Modernity 1. Climate Change, Civil Progress, and Rational Evolution, Martin Schonfeld (University of South Florida, USA) 2. Nature in the Active Voice, Val Plumwood (Australian National University, Australia) 3. Climate Change and Nihilism: Living in the Zone of Nihilism, Ruth Irwin (University of Auckland, New Zealand) Part II: Transforming Global Politics 4. Transforming Worldviews to Cope with a Changing Climate, Leo Elshof (Acadia University, Canada) 5. Education at the End of Nature: Learning to Cope with Climate Change, Timothy Luke (Virginia Tech, USA) 6. Education Against Climate Change: Information and Technological Focus Are Not Enough, Edgar Gonzalez Gaudiano (Autonomous University of Nuevo Leon, Mexico) Part III: Global Environmental Justice 7. Global Climate Change, Adaptation and Abatement in a Context of Risk and Vulnerability, Leslie LeGrange (Stellenbosch University, South Africa) and Heila Lotz-Sisitka (Rhodes University, South Africa) 8. Gender and Climate Change: An Environmental Justice Perspective, Patricia Glazebrook (Dalhousie University, Canada) Part IV: Liberal Responsibility 9. Mediated Responsibilities, Global Warming and the Scope of Ethics, Robin Attfield (Cardiff University, UK) 10. Transforming Attitudes to Environmental Law in Light of Climate Change, Murray Sheard (Director of Professional Integrity Training, Tiri, UK) Bibliography Index.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".