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Record W4224107272 · doi:10.1080/02723638.2022.2039433

Mobilizing legal expertise in and against cities: urban planning amidst increased legal action in Bogotá

2022· article· en· W4224107272 on OpenAlexaff
Luisa Sotomayor, Sergio Montero, Natalia Ángel-Cabo

Bibliographic record

VenueUrban Geography · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsYork University
FundersUniversidad de los Andes
KeywordsContext (archaeology)PoliticsAction (physics)Urban planningPublic administrationPolitical scienceLegal actionPublic relationsEnvironmental planningLawGeographyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

In the past decade, there has been a rise in legal action around urban policy and planning in Colombia. Legal expertise has been mobilized by a plethora of actors, including social movements, local politicians, neighborhood groups and individual citizens. This has resulted in legal experts and judges often dictating how social housing, transport corridors, public space, or waste management schemes ought to be implemented by municipal administrations. In this context, mayors and planners complain that the increasing involvement of the judiciary in urban planning drains local resources and undermines the power of mayors to set and implement the political agenda they were democratically elected to execute. In this article, we analyze the rise of legal action against urban planning in Bogotá and conclude by proposing a research agenda for socio-legal and urban scholars interested in further exploring the potential implications of the increasing mobilization of legal expertise in urban planning.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0130.014
Scholarly communication0.0080.003
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.233
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations20
Published2022
Admission routes1
Has abstractyes

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