Bibliographic record
Abstract
The Nobel Prize Committee for Economics has been pretty good about drawing attention to innovative developments in economics since 1969, but in the past 20 years only one prize has been awarded for work that had a strong natural resources dimension, namely that on ad hoc cooperative solutions to the management of common property resources (Elinor Ostrom, University of Indiana). At least three Peace Prizes have been awarded for contributions to issues in conservation and the environment (Al Gore, the IPCC, and Wangari Maathai in Kenya for a tree-planting campaign). Toronto's Globe and Mail newspaper speculated in October 2012 that William Nordhaus would win the prize in economics, probably for his simulation model of economic growth and global warming (an Integrated Assessment Model). But to date only Ms Ostrom has been awarded the Economics Prize for essentially environmental economics. Thomas Schelling has circled back to environmental issues in his research over the years and was awarded the prize in economics, but it was for his contributions to game theory that he was singled out. Ronald Coase (Economics Nobel winner) focused the attention of economists on possible ‘markets’ for externalities, among other things, but few would refer to him as an environmental economist. Robert Solow set out the basic model of economic sustainability but again his prize was for other contributions. Observers in Stockholm and Oslo have thus made known their concern for environmental issues but have remained fairly agnostic about the significance of work by resource and environmental economists on such issues.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.300 | 0.259 |
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".