Designing for Agonism: 12 Workers' Perspectives on Contesting Technology Futures
Bibliographic record
Abstract
In this paper, we gather 12 workers from a large technology company, as recent participants of a research initiative on the social impact of emerging technologies, to present a collaborative analysis of the opportunities and limitations of dissensus-based approaches to technology research and design. We introduce a series of speculative and deconstructive probes and present findings from their use in four collaborative design sessions. We then draw on the theoretical tradition of Agonism to identify moments of friction, refusal, and disagreement over the course of these sessions. We contend that this approach offers a politically important alternative to consensus-based collaborative design methods and can even surface new rhetorics of contestation within discourses on technology futures. We conclude with a discussion of the importance of worker-authored research and an initial set opportunities, challenges, and paradoxes as a resource for future efforts to "Design for Agonism."
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.038 | 0.044 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".