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Record W4395086387 · doi:10.1017/9781009243728.015

Revisiting the Ethical-Political Perspective in Technology Design

2024· book-chapter· en· W4395086387 on OpenAlexaff
Ellen Balka, Ina Wagner, Anne Weibert, Volker Wulf

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)PoliticsEngineering ethicsPolitical scienceSociologyEnvironmental ethicsEngineeringComputer sciencePhilosophyLawArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter revisits the ethical-political perspective on technology design. Feminist/intersectional approaches to the design of IT artifacts build on practices developed in participatory design and action research, enriching them with norm-critical, norm-creative, and social justice-oriented perspectives. Practice-based design adds experiences with designing flexible, malleable systems that are open to end-user development, offering technological tools for designing systems that are open to other ways of thinking and doing (work). Decolonizing approaches contribute to doing justice to parts of the world that experience(d) oppression and marginalization, discarding the needs of people and disrespecting their knowledge. Among the specific challenges of a feminist/intersectional approach to design are the need to make invisible aspects of work visible; to recognize women’s skills without falling into the trap of gender stereotyping; to engage in improving working conditions; to defend care against a managerial logic, take care of the many overlooked and undervalued aspects of work in design, but also to care for research subjects and create safe spaces.

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)0.015
metaresearch head score (Gemma)0.008
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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.045
Scholarly communication0.0140.014
Open science0.0010.005
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.064
GPT teacher head0.318
Teacher spread0.254 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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