Bibliographic record
Abstract
Saskatchewan, Canada, is a province with strong traditions of volunteerism and innovation. In 2001, 36 per cent of its 1 million population was rural, though this was significantly lower than in 1951 when it was 70 per cent (Statistics Canada 2005). Saskatchewan is experiencing higher population growth in urban than in rural regions. Many rural communities are developing methods to attract migrants and retain their current population by taking advantage of the current resource booms and tourism. Rural programs aimed at education for sustainable development (ESD) are typically more integrated and smaller in scale than those in urban centres. The smaller-scale ESD in rural communities provides a greater capability to innovate more rapidly, while urban centres have entrenched political and market interests, regulatory boundaries and economic barriers to change. Using an inclusive and transparent structure, the Regional Centre of Expertise (RCE) for ESD in Saskatchewan focuses heavily on cooperation between rural and urban communities. Integrative strategies, with objectives spanning both rural and urban areas, are advanced using a nontraditional, nonhierarchical governance model. A flexible structure gives participants the freedom to explore research topics and activities of interest originally within six ESD theme areas collectively identified. The governance model recognises that some centralised decision-making is important, but that these mechanisms must be responsive to members in a flexible way.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.049 |
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".