Bibliographic record
Abstract
The recent United Nations Conference on the Environment and Development, popularly known as the Earth Summit, was a milestone event for sustainable development. In dealing with ecological and developmental issues concurrently, it brought the international environmental agenda to the fore. Canada was the first industrialized country to announce that it would be a signatory to the Biodiversity Convention, and by furthering future forestry and global warming conventions, it played an important leadership role. Achieving Sustainable Development explores how well Canada has met the Earth Summit's targets and attempts to find ways in which the public can become involved in such issues. Its authors stress the importance of integration of information from various fields and seek to stimulate the exchange of knowledge among the academic community, government, non-governmental organizations and industry. The contributors look far beyond merely identifying and analyzing selected issues and problems. To facilitate public discussion and to affect policy development, at least one initiative is proposed and detailed for each problem identified. Achieving Sustainable Development provides an overall introduction to critical subjects in sustainable development -- industrial growth, women, institutional arrangements, industrial practices, and aboriginal peoples. Most importantly, it argues for the immediate development of a research and policy agenda for Canada and suggests mechanisms for its implementation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".