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Record W2555831167 · doi:10.1145/2992154.2992163

UD Co-Spaces

2016· article· en· W2555831167 on OpenAlexafffund
Narges Mahyar, Kelly J. Burke, Jialiang Xiang, Siyi Meng, Kellogg S. Booth, Cynthia Girling, Ronald Kellett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsComputer-supported cooperative workUrban designComputer scienceHuman–computer interactionNeighbourhood (mathematics)Co-designUser-centered designProcess (computing)Domain (mathematical analysis)Urban planningDesign processKnowledge managementWork (physics)EngineeringWork in process

Abstract

fetched live from OpenAlex

UD Co-Spaces (Urban Design Collaborative Spaces) is an integrated, tabletop-centered multi-display environment for engaging the public in the complex process of collaborative urban design. We describe the iterative user-centered process that we followed over six years through a close interdisciplinary collaboration involving experts in urban design and neighbourhood planning. Versions of UD Co-Spaces were deployed in five real-world charrettes (planning workshops) with 83 participants, a heuristic evaluation with three domain experts, and a qualitative laboratory study with 37 participants. We reflect on our design decisions and how multi-display environments can engage a broad range of stakeholders in decision making and foster collaboration and co-creation within urban design. We examine the parallel use of different displays, each with tailored interactive visualizations, and whether this affects what people can learn about the consequences of their choices for sustainable neighborhoods. We assess UD Co-Spaces using seven principles for collaborative urban design tools that we identified based on literature in urban design, CSCW, and public engagement.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score1.000

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)0.0010.002

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2016
Admission routes2
Has abstractyes

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