Bibliographic record
Abstract
Introduction PART ONE: FORGING THE INDUSTRIAL WATERFRONT 1 Planning for Change: Harbour Commissions, Civil Engineers, and Large-Scale Manipulation of Nature MICHAEL MOIR 2 Establishing the Toronto Harbour Commission and Its 1912 Waterfront Development Plan GENE DESFOR, LUCIAN VESALON, AND JENNEFER LAIDLEY 3 From Liability to Profitabilit: How Disease, Fear, and Medical Science Cleaned Up the Marshes of Ashbridge's Bay PAUL S.B. JACKSON 4 From Feast to Famine: Shipbuilding and the 1912 Waterfront Development Plan MICHAEL MOIR 5 A Social History of a Changing Environment: The Don River Valley, 1910--1931 JENNIFER BONNELL 6 Boundaries and Connectivity: The Lower Don River and Ashbridge's Bay TENLEY CONWAY 7 Networks of Power: Toronto's Waterfront Energy Systems from 1840 to 1970 SCOTT PRUDHAM, GUNTER GAD, AND RICHARD ANDERSON PART TWO: SHAPING THE POST-INDUSTRIAL WATERFRONT 8 Creating an Environment for Change: The 'Ecosystem Approach' and the Olympics on Toronto's Waterfront JENNEFER LAIDLEY 9 From Harbour Commission to Port Authority: Institutionalizing the Federal Government's Role in Waterfront Development CHRISTOPHER SANDERSON AND PIERRE FILION 10 Cleaning Up on the Waterfront: Development of Contaminated Sites HON Q. LU AND GENE DESFOR 11 Who's in Charge?: Jurisdictional Gridlock and the Genesis of Waterfront Toronto GABRIEL EIDELMAN 12 Public-Private Sector Alliances in Sustainable Waterfront Revitalization: Policy, Planning, and Design in the West Don Lands SUSANNAH BUNCE 13 Socio-ecological Change in the Nineteenth and Twenty-first Centuries: The Lower Don River GENE DESFOR AND JENNIFER BONNELL References Contributors Index
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".