Urban futures of digitalized fossil labour: mutual articulations of smartness in cities and extractive work
Bibliographic record
Abstract
Little research to date has drawn out the connections between digitalizing extractive labour and digital urbanism. In this article, we argue that these are complementary, mutually reinforcing processes: digital urbanism enables the digitalization of extractive labour, and digitalization centralizes extractive labour in urban centres. More specifically, ‘smart’ digitalization as a political-economic strategy works on both the extractive labour and urban levels, as seen in smart cities as well as extraction’s intensifying use of automation, sensors, digital twins, artificial intelligence, and robotics. Here, we draw on a four-year ongoing case study of the digitalizing oil and gas industry in the city of Calgary and the province of Alberta, Canada, to situate the dual smartness strategy within a shared process of future world building. The Calgary smart city promises a future economic diversification from the oil and gas industry toward high technology, and digitalizing oil and gas labour promises a low-carbon future. What seems to be happening empirically, however, is that digitalization in Calgary is not reducing extraction’s centrality to Alberta’s economy, but coming to coexist with it through ‘cleantech’ economies. Besides theorizing this dual smartness, here we show that smartness operates as a pervasive epistemological framing rather than a mere set of formal municipal programmes. We also suggest that while the production and unrolling of smart futures is a quotidian process in practice, the potential of a revolutionary future discursively creates the conditions for the everyday futuring.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".