Conclusion
Bibliographic record
Abstract
Socio-environmental research has a rich legacy. Scholarship has evolved to be more interdisciplinary, as long before. Sustainability science builds on von Humboldt, Marsh, and Meadows. Research on social–ecological systems research is informed by Ostrom; resilience by Holling; vulnerability by White, Sen, and Beck; and CHANS by Marsh and Moran. Ecological economics emphasizes the economy as a subset of the Earth, leveraging Ricardo, Jevons, and Daly. Ecosystem services research, informed by Ehrlich and Odum, quantifies benefits from ecosystems. Industrial ecology views industrial systems ecologically, as done by Graedel, Ayres, and Kneese. Political ecology focuses on power relations, as did Marx, Polanyi, Shiva, and Blaikie and Brookfield. Environmental justice, pioneered by Bullard, considers unequal benefits and harms. Other systems research focuses on a given context, as on cities (Childe, Mumford, and McDonnell and Pickett), land (Melville), and food (Liangji, Malthus, Boserup, and Ho). Integrated assessments build on Meadows. Planetary and Anthropocene perspectives focus on the global scale (see Hutchinson, Boff). Legacy readings can help frame socio-environmental relationships and enrich collaborations.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.085 |
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".