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Record W3004014642 · doi:10.4324/9781315545011-28

Urban entrepreneurship through transactive planning: The making of Waterfront Toronto

2016· book-chapter· en· W3004014642 on OpenAlexaboutno aff
Matti Siemiatycki

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsTransactive memoryEntrepreneurshipEnvironmental planningBusinessGeographyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.042
GPT teacher head0.218
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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