Index
Bibliographic record
Abstract
54-6, 60 Asia see individual countries Atyi, R. and M. Simula 66, 71, 74 Australia arable land losses to salinity 238 Argyle Diamonds and competitive advantage 251 commercial over social rates of discount 252, 253 competition policy 241-3, 248-57 competitive neutrality failure, penalties for 252 discounting rates 245-7, 254-7 Environment Protection and Biodiversity Conservation (EP&BC) Act 243, 260 environmental governance and discounting 235, 236, 237, 238, 240-43, 244-7, 254-7 forest certification programmes 81 forestry deregulation and public interest 242-3 indigenous community and minerals extraction royalties 255 institutional environmental governance 240-43 internal rate of return (IRR) discount rate 237 Kalgoorlie-Boulder water supply 246-8 market economy 241, 242 National Competition Policy (NCP) 241-3, 248-57 native forestry and competitive neutrality 251-2 Regional Forests Agreements (RFAs) 243, 260 royalty revenues from natural resources extraction 253-7 social perceptions of the environment 240 sustainable development strategy 243, 250, 252-3 voter response to environmental governance 257 water provision and pricing policy 32, 242, 243, 246-8, 254, 255 Austria
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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.597 | 0.370 |
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".