Radical Green Political Theory and Land Use Decision Making in the Region of Waterloo
Bibliographic record
Abstract
Problems have arisen at the intersection of environmental assessment and land use planning in Ontario for two reasons. Established land use planning practices have failed to satisfy growing environmental concerns about individual undertakings and their cumulative effects. And environmental assessment, as an environmentally sensitive approach to planning, now both overlaps inefficiently with some land use planning decisions, and is in some ways attractive for broader application in planning decision making. These two factors have led to a search for solutions. Some wish to apply environmental assessment requirements more broadly in land use planning decision making. Others favour merging the processes in the relatively small area where environmental assessment and land use planning requirements already overlap. Broader examination may reveal further possibilities. The Environmental Assessment and Planning Project, initially funded by the Social Sciences and Humanities Research Council of Canada, aims to develop a better understanding of the existing problems and the needs and options for reform. The work completed thus far includes case studies of major controversies and responses to these controversies in Ontario and British Columbia. Radical Green Political Theory and Land Use Decision Making in the Region of Waterloo is the report of one of these studies. For other case studies and publications of the project, contact the project coordinator and general editor of the case study series, Dr. Robert
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".