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Record W2903423101 · doi:10.7577/formakademisk.2608

Systemic design agendas in education and design research

2018· article· en· W2903423101 on OpenAlexaff
Susu Nousala, David Ing, Peter Jones

Bibliographic record

VenueFormAkademisk - forskningstidsskrift for design og designdidaktikk · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsSociotechnical systemTransdisciplinarityEngineering ethicsSociologyManagement scienceComputer scienceEngineeringKnowledge managementSocial science

Abstract

fetched live from OpenAlex

Since 2014, an international collaborative of design leaders has been exploring ways in which methods can be augmented, transitioning from the heritage legacy focus on products and services towards a broad range of complex sociotechnical systems and contemporary societal problems issues. At the RSD4 Symposium (2015), DesignX co-founder Don Norman presented a keynote talk on the frontiers of design practice and necessity for advanced design education for highly complex sociotechnical problems. He identified the qualities of these systems as relevant to DesignX problems, and called for systemics, transdisciplinarity and the need for high-quality observations (or evidence) in these design problems. Initial directions found were proposed in the first DesignX workshop in October 2015, which were published in the design journal Shè Jì. In October 2016, another DesignX workshop was held at Tongji University in Shanghai, overlapping with the timing of the RSD5 Symposium where this workshop was convened. The timing of these events presented an opportunity to explore design education and research concepts, ideas and directions of thought that emerged from the multiple discussions and reflections through this experimental workshop. The aim of this paper is to report on the workshop as a continuing project in the DesignX discourse, to share reflections and recommendations from this working group.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.106
GPT teacher head0.362
Teacher spread0.256 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations6
Published2018
Admission routes1
Has abstractyes

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