Urban entrepreneurship through transactive planning: The making of Waterfront Toronto
Bibliographic record
Abstract
In 1993, John Friedmann published a widely cited commentary outlining his vision of a future direction for planning. He called it “Toward a non-Euclidian mode of planning” (Friedmann 1993). This commentary built on his groundbreaking theory of transactive planning developed in Retracking America (1973), and his philosophy for mobilizing knowledge into action in Planning in the Public Domain (1987). For Friedmann, the old style of Euclidean planning was rooted in 19th- century views of planning as a form of engineering science. In the old planning style, the assumption is that decisions are being made rationally and comprehensively. The objective of planning, from this perspective, is developing long-term plans, simulations and blueprints based on advanced analytic methods. This gives decision-making a sheen of scientific rigor. Yet this old model of planning is particularly unsuited for turbulent times, and contexts where organizations and individuals are in a constant state of flux. In Friedmann’s proposed non-Euclidean model, planning is less about developing plans and preparing documents to chart a long-term course, and more a way of bringing knowledge to bear on action in the here and now. To achieve this end, Friedmann argued for an approach to planning that privileges the immediate timeframe and the local or regional scale. It has five key characteristics: 1 Normative and infused with values about inclusiveness, respect for the natural world, fairness and equality.
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".