Bibliographic record
Abstract
All human societies have laws, which may be written or unwritten. Those laws, and the mechanisms to enforce them, evolve as internal and external forces shape the society. Modern environmental regulatory frameworks are a complex mixture of traditional behavioural rules and newer benchmarks of environmental performance. Gradually, we have come to value the rules themselves above the goals they are intended to achieve. In fact, environmental improvement can be achieved in many ways, not just through traditional regulatory approaches. Traditional "command-and-control" regulation provides a useful backstop but is limited in its ability to encourage innovation. Newer approaches, including economic instruments, voluntary clean-up, and recognition programs, offer the means to encourage prevention, protection, and conservation, rather than resource wastage and reliance on end-of-pipe technology. A combination of command-and-control programs for minimum limits, coupled with economic incentives and voluntary compliance schemes for enhanced protection, may be the only viable environmental management strategy for the 21st century.Key words: environmental management, environmental law, pollution prevention, economic instruments, voluntary, compliance.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".