‘Someone who uses it better’: Speculative fiction as method for AI work refusal in cultural geography
Bibliographic record
Abstract
Work practices and workplaces are central to discourses about artificial intelligence (AI). This centrality is reflected in the ongoing introduction of AI into existing work processes and popular predictions about how AI will lead to job replacement in the future of work. We present a short speculative narrative that mediates between dystopian and utopian depictions of AI and its refusal to loosen dominant tropes that characterize AI’s uptake in workplaces. We suggest that narrative can be a method or practice for doing cultural geography, and our narrative offers a glimpse into our protagonist’s working life in an AI-dominated future and her more and less subtle acts of refusal therein. Informed by critical research in feminist economic geography and beyond on the future of work, our narrative dramatizes, in hopefully unexpected ways, what an AI future of work (refusal) looks like.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.074 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".