The Lake Ontario Waterfront Trail, Canada: Integrating natural and built environments
Bibliographic record
Abstract
Ingrid Leman Stefanovic is Associate Professor of Philosophy at St.Michael's College, University of Toronto, Canada. She is also a full member of the graduate Institute for Environmental Studies and teaches interdisciplinary courses in Environmental Decision Making and Environmental Philosophy. Research interests address how taken for granted values and perceptions affect decision making and policy development. Previously, she worked in Leman Group Inc., together with her father, Alexander B. Leman. She has co-edited a book on the Great Lakes Megalopolis. Her most recent book is entitled Safeguarding Our Common Future: Rethinking Sustainable Development (SUNY Press, 2000). She is a member of the World Society for Ekistics (WSE). The text that follows is a slightly edited and revised version of a paper that the author intended to present at the WSE Symposion "Defining Success of the City in the 21st Century," Berlin, 24-28 October, 2001, in which she was finally unable to participate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".