Green Fiscal Reform: Protecting our Natural Resources for a Sustainable Future
Bibliographic record
Abstract
This international conference on taxation and the environment is exquisitely timed. It is two and a bit months before the Paris conference of the United Nations Framework Convention on Climate Change. It is a bit over one week after the elevation, after two years of aberration, of an Australian Prime Minister, who is committed to respect for science and to policy based on rigorous analysis of the public interest. On Paris, over the past year, heads of government of major economies—Presidents Obama, Xi, Park and Hollande; Chancellor Merkel; Prime Ministers Cameron and Abe—have given consistently firm preparatory support to a strong outcome. Two G20 heads of Government set out to swim against the tide of leadership opinion and policy on climate change in the major economies. Both learned to respect the strength of the tide and this year have swum across rather than directly into its full force. Maybe soon neither will be swimming on this beach at all. Prime Minister Abbott’s time has passed. Prime Minister Harper of Canada seems set to lose his Parliamentary majority before the Paris meeting. Former Vice President Gore at a seminar at the University of Melbourne in July expressed conditional regret about the likely outcome in Paris. “A legally binding, comprehensive
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".