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Record W2751631292 · doi:10.1080/09613218.2017.1358569

Rethinking sustainability frameworks in neighbourhood projects: a process-based approach

2017· article· en· W2751631292 on OpenAlexaff
Amy A. Oliver, Daniel Pearl

Bibliographic record

VenueBuilding Research & Information · 2017
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSustainabilityFraming (construction)Software deploymentProcess managementNeighbourhood (mathematics)Process (computing)Sustainability organizationsManagement scienceEngineeringComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

A process-based approach is used to examine the deployment of two neighbourhood-scale sustainability assessment systems and their tools in two European cities. It explores how these sustainability assessment systems and their tools are contextualized within a larger design and planning process by considering how stakeholders collaborate, how they set design problems, how they make decisions and how they propose design solutions in the early design phases. Specifically, using interviews conducted with key project actors in Malmö, Sweden, and Barcelona, Spain, this paper examines how the sustainability assessment systems in each respective case study were framed, and how this framing impacted upon the design process. A key finding is that how sustainability assessment systems are framed has just as significant an impact as the nature of the tools, especially on the processes entailed. Community participation can have a strong, positive impact on the design process and on outcomes. The case studies confirm that context and players cannot be divorced from tools.

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)0.065
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0070.026
Scholarly communication0.0170.012
Open science0.0040.014
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.360
Teacher spread0.320 · 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 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

Citations43
Published2017
Admission routes1
Has abstractyes

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