Bibliographic record
Abstract
Global calls for action on climate change have become more urgent in recent years. However, how to act to achieve climate sustainability remains elusive. The evidence is clear that governmental initiatives – global, national, and provincial – have not been able to coalesce into a meaningful strategy for climate sustainability. What is required is a shift in climate responsibility from governments to individuals and communities who think globally but are best able to act locally. To encourage the citizenry to act requires a science-based information and education whereby climate action is clearly defined along with the consequences of actions (or inaction). Education must include a climate curriculum as a mainstream subject in our schools. Using this approach, local community baselines of climate information, vulnerability, and adaptive capacity can be established. In enhancing their climate roles, governments’ need to shift from carrying out mandates for climate response, to becoming auditors of carbon use in which citizens and businesses are given incentives to reduce carbon footprints. Finally, increased investments need to be directed to communities so that they can take more responsibility and be more prepared to live with climate change impacts. Governments also need to engage the community in participatory strategic long-term planning for adaptation to the changing climate. Keywords: climate action, climate responsibility, institutional arrangements, science-based information, education legacy, strategic planning, community investment
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| 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".