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Record W2920623893 · doi:10.21606/drs.2018.355

Learning from Feminist Critiques of and Recommendations for Industrial Design

2018· article· en· W2920623893 on OpenAlexaff
Isabel Prochner, Anne Marchand

Bibliographic record

VenueProceedings of DRS · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFeminist philosophyIndustrial designPerspective (graphical)Field (mathematics)SociologyFeminist theoryFeminismEngineering ethicsPower (physics)Theme (computing)EpistemologyManagement scienceGender studiesComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper shows how a feminist perspective can inform industrial design theory and practice. It provides a list of feminist-informed critiques and proposals toward industrial design based on a literature analysis of existing feminist work in industrial design and analysis of three feminist-driven co-design projects. The results show that a feminist perspective identifies systemic problems in industrial design based on the presence of power and masculinity, unequal power dynamics between people and negative situations facing women. These problems appear at the multiple levels of industrial design and are a theme throughout feminist critiques in the field. In turn, feminist recommendations are typically grass roots, relying on actor interventions that draw on women’s perspectives and/or feminist perspectives. These results offer a range of contributions to industrial design. Broadly speaking, they offer an alternative perspective to industrial design to help the field move forward and respond to social imperatives. The specific critiques and recommendations can also be broadly applied, as they pinpoint problems within the field and guide alternative practices.

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)0.047
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0130.066
Scholarly communication0.0150.013
Open science0.0040.007
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0090.002

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 head0.160
GPT teacher head0.300
Teacher spread0.140 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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