The Capital Regional District Growth Strategy: Herding Cats onto the Road to Sustainability
Bibliographic record
Abstract
Over the past decade or so, several Canadian jurisdictions have moved to integrate environmental considerations more effectively in land use planning. Many of the most promising initiatives have been in the southern parts of Ontario and British Columbia, which face significant population increases and associated urban pressures. The approaches taken by government authorities and citizens in these two areas have differed significantly in their application of environmental assessment and planning principles, their adoption of authoritative and consensus-based processes and their response to provincial action and community initiatives. The Assessment and Planning research project, initially funded by the Social Sciences and Humanities Research Council of Canada, seeks to learn from experiences in the two provinces. Part of the work centres on a series of case studies covering a range of initiatives in the two provinces. The Capital Regional District Growth Strategy: Herding Cats onto the Road to Sustainability is the report on the sixth British Columbia study. For other case studies and publications of the project, contact the project co-ordinator and case study series editor, Dr. Robert Gibson, Environment and Resource Studies (ERS),
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".