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
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".