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Record W2606853450 · doi:10.1386/adch.16.1.125_1

Design, and design education: How can they get together?

2017· article· en· W2606853450 on OpenAlexaff
Jorge Frascara

Bibliographic record

VenueArt Design & Communication in Higher Education · 2017
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDesign educationCreativityEngineering ethicsDisciplineWork (physics)Computer scienceSociologyPsychologyEngineeringSocial psychologyBusinessSocial science

Abstract

fetched live from OpenAlex

Abstract Designers work with people and for people, and the designers’ main objective is to transform people’s existing situations into better ones. This is not easy: it requires both disciplinary and interdisciplinary knowledge, and a will to do things well. Focusing on people makes the social sciences become a necessary support for design, and forces design to become an accountable profession, where decisions are based on reliable criteria, and where projects respond to important needs of society. In most design programmes today a simplistic conception of creativity keeps design as an easy and self-serving activity, where students are kept busy with formal/visual and technological concerns. Unfortunately studies of people are normally missing from design education, along with language and evaluation of performance. If design is to develop its full potential as a major contributor to society’s well-being, design education has to change into a rigorous, interdisciplinary and socially responsible activity.

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)0.040
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.056
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.005
Science and technology studies0.0070.028
Scholarly communication0.0340.041
Open science0.0030.024
Research integrity0.0130.013
Insufficient payload (model declined to judge)0.0240.006

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 head0.149
GPT teacher head0.393
Teacher spread0.244 · 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 designNot applicable
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

Citations24
Published2017
Admission routes1
Has abstractyes

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