Despite Some Success Stories, Policy Measures Lag Behind
Bibliographic record
Abstract
Introduction: some policy success stories exist, but they are not enough Examples of effective environmental policies and improvements exist, from the global to local levels – especially where problems have been well identified and are manageable, and where regulatory and technological solutions are readily available (well established, but incomplete) . All stakeholders (global institutions, local and national governments, businesses, communities and individuals) have roles to play in addressing environmental challenges. This section concentrates on responses at the international level and by national governments, but also acknowledges the important role of the private sector and local initiatives. New policies will be required if challenges are to be met effectively. However, much can also be achieved through more effective implementation and enforcement of existing policies and standards. ﹛Chapters 12 to 17; SPM Section 2﹜ A growing framework of international agreements and initiatives covers various aspects of issues related to air, freshwater, land and soil, oceans and coasts, and biodiversity which are linked in an integrated manner through the SDGs . However, while individual agreements can address relatively simple issues, more societally and ecologically complex problems face major political opposition, resulting in reduced clarity regarding goals, targets, indicators, instruments, concomitant funding, monitoring and enforcement measures. Furthermore, these instruments are mostly reactive and have not yet targeted emerging issues such as sand mining, plastic in the oceans, antibiotic resistance, international land grabbing and many others. ﹛18.1﹜ Among the examples of effective policies is the Montreal Protocol, which led to the decrease of emissions of ozone-depleting substances (well established). The ozone hole over Antarctica in September and October 2019 was the smallest observed at any time since 1982. Many regulatory measures, including local and global environmental treaties and laws, have been qualified successes (established but incomplete). Many countries are using local or national regulatory measures to begin to reduce (if not yet control) plastic pollution. In many countries the coverage of terrestrial and marine protected areas has increased ( Figure 4.1 ). Transnational agreements on trade in waste and hazardous waste dumping have had measurable positive effects. ﹛14.2.2﹜ With regard to the effectiveness of goal-setting, the Millennium Development Goals (MDGs) mobilized action at the local and global levels (established but incomplete).
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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 | 0.038 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".