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Record W2744419405 · doi:10.1080/01426397.2017.1355446

Vacancy as a laboratory: design criteria for reimagining social-ecological systems on vacant urban lands

2017· article· en· W2744419405 on OpenAlexaff
Kees Lokman

Bibliographic record

VenueLandscape Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEcosystem servicesDemocracyEnvironmental resource managementRange (aeronautics)Environmental planningUrban planningUrban designEcosystemEcologyGeographyCivil engineeringEnvironmental scienceEngineeringPoliticsPolitical science

Abstract

fetched live from OpenAlex

The complex socio-economic conditions underlying (temporary) vacant urban landscapes have produced a wide range of spatial outcomes. Solutions to address these diverse spatio-temporal conditions inherently call for a range of design approaches. This paper, through literature and project review, introduces a conceptual design framework consisting of four criteria integral for developing sustainable solutions for repurposing vacant urban lands: (1) environmental justice and ecological democracy; (2) ecosystem services and urban biodiversity; (3) aesthetic experiences, and; (4) programming. By examining five case studies, I reveal a number of different and innovative ways in which these criteria can be integrated and deployed to transform urban vacant lands. Here, vacancy becomes a laboratory for testing and implementing new social-ecological systems across a range of spatial and temporal scales. This requires experimentation in the development of alternative planning and design strategies, including new public participation models, policy frameworks and funding mechanisms.

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)0.030
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0060.007
Open science0.0040.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.306
GPT teacher head0.505
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations23
Published2017
Admission routes1
Has abstractyes

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