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Record W3133939639 · doi:10.21606/drs.2010.24

Everyday People: Enabling User Expertise in Socially Responsible Design

2010· article· en· W3133939639 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of DRS · 2010
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

This paper examines the contemporary relevance of interdisciplinary research practice specifically within the field of design for social need. Examining the complexity of current social problems using the concepts of Rittel & Webber’s wicked problems, this paper looks at the potential for the application of co-design methods within an interdisciplinary framework. By proposing the use of a social model of design, it is argued that it is through co-design methods and the use of generative toolkits such as Liz Sanders’ MakeTools and IDEO’s Human-Centered Design Toolkit that the design process can be enhanced in the early stages. This paper argues for interdisciplinary practice by enabling user expertise so that the user can equally contribute to the design process. This paper also explores the changing role of the designer from researcher to facilitator, and how this can benefit communities dealing with complex problems. Finally, this paper looks at the benefits of active user involvement in socially responsible design through discussions on empathy, user empowerment and benefits to communities within design education.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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