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Upholding the Poetic in Design Collaboration

2005· article· en· W2597352195 on OpenAlexfundno aff
Jane Burry, Andrew Burrow, Mark Burry

Bibliographic record

VenueProceedings of the International Conference on Computer-Aided Architectural Design Research in Asia · 2005
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
FundersAustralian Research CouncilSimon Fraser UniversityRMIT University
KeywordsComputer scienceReciprocalProcess (computing)Knowledge managementHuman–computer interactionRepresentation (politics)Object (grammar)Computer-supported cooperative workInformation sharingDesign processProcess managementWorld Wide WebWork in processEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Design is a fundamentally collaborative activity. It commonly calls on a wide range of expertise and is arguably most effective when all contributions can be considered from an early and highly conceptual phase of the process. The sharing of information, particularly in a process that, at its best, involves collective conceptualisation is complicated by the very close and reciprocal relationship between the partial knowledge about the object of design and the mode of expression or representation of these ideas. As the design process and its numerous inputs, iterations and interrelationships become embedded in the communications; knowledge capture, management and access become central issues. This paper will selectively recount some of the substantive evidence for the characteristics of communication environments most supportive to design collaboration. In response to these findings it will introduce the use of wiki as the basis of an environment to provide this support, provide more detailed examples of the ways in which wiki has been adopted in early collaborative experiments and describe the developments currently being implemented, and how these are being tested in use.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.147
GPT teacher head0.366
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations3
Published2005
Admission routes1
Has abstractyes

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