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Record W2948672863 · doi:10.1177/0308518x19856628

Land developers as institutional and postpolitical actors: Sites of power in land use policy and planning

2019· article· en· W2948672863 on OpenAlexaffabout
Donald Leffers, Gerda R. Wekerle

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork UniversityCarleton University
Fundersnot available
KeywordsComplementarity (molecular biology)Corporate governanceCitizen journalismPolitical sciencePoliticsInstitutional analysisLand usePublic relationsSociologyPublic administrationEnvironmental planningEconomicsManagementSocial scienceGeographyEngineering

Abstract

fetched live from OpenAlex

Land developers play an active role as institutional actors that shape the development and governance of urban regions. In this paper, we find that developers not only influence state institutions governing land use, they are place-based actors whose influence is normalized as invited strategic stakeholders in planning exercises. Our analysis highlights the complementarity of institutional and postpolitical theories in offering a nuanced understanding of the multi-faceted and multi-scalar relationships among powerful actors engaged in land development processes. Postpolitical theories highlight the participatory processes of inclusion and exclusion in collaborative-based planning exercises that privilege certain stakeholders and exclude others. Through the lens of institutionalist theory, we move beyond specific land conflicts to focus on the day-to-day interactions and institutionalized roles of key actors, ideas and political influences in shaping contested land policy and outcomes. The analysis is based on multi-year research projects that drew upon interviews, observation and document analysis of key actors’ engagement in initiatives to formulate and implement growth management policies in the Toronto region.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.428

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)0.0000.000

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 head0.017
GPT teacher head0.246
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations34
Published2019
Admission routes2
Has abstractyes

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