Bibliographic record
Abstract
The sheer scale of human changes to the global biosphere now requires that discussions of governance move beyond traditional notions of environmental protection, parks, pollution, and population. Globalisation isn’t just a matter of economic, political, and cultural boundary crossings, but now has to be understood as a matter of material transformation. The environment is no longer ‘out there’ as the given context for humanity; globalisation has changed that both conceptually and in terms of how governance now needs to operate to shape the future conditions for human life in an interconnected and rapidly changing biosphere. Globalisation has, among other things, substantially rearranged the species mix in most of the fertile parts of the terrestrial biosphere, dramatically changed ocean ecosystems through industrial scale fishing and is now setting in motion disruptive climate changes too. While sustainability and resilience have become the new terms for conceptualising environmental governance, the debates surrounding them have, as yet, not really begun to grapple with the big questions of who should decide what the immensely productive industrial systems of the global economy should actually produce, and how ecosystems are to be reconstructed to provide a more stable backdrop for the new forms of urban human life that globalisation is constructing. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".