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Record W2976990601 · doi:10.1162/desi_a_00559

On the Need for Mapping Design Inequalities

2019· article· en· W2976990601 on OpenAlexaff
Mona Sloane

Bibliographic record

VenueDesign Issues · 2019
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsInequalityPrivilege (computing)CreativitySociologyPower (physics)Computer scienceEpistemologyManagement scienceEngineering ethicsEngineeringMathematicsSocial psychologyComputer securityPsychology

Abstract

fetched live from OpenAlex

This article introduces the special issue “Design Inequalities” and the articles contained within it. It provides a new conceptual framework by looking at design as a way of “making society” and exploring how design is entangled with social inequalities across a wide spectrum of domains. The ontological point of departure is to view “design” and “inequalities” as both superstructures and micro-practices of creativity, power, and expertise that can simultaneously sustain and challenge privilege and exclusion, regardless of what is being designed.

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)0.031
metaresearch head score (Gemma)0.133
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: Commentary · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.133
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0050.036
Scholarly communication0.0160.041
Open science0.0040.014
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0160.003

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 head0.110
GPT teacher head0.296
Teacher spread0.186 · 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
GenreCommentary

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

Citations26
Published2019
Admission routes1
Has abstractyes

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