“Make our communities better through data”: The moral economy of smart city labor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart cities are now an established context in which data and digital technologies shape urban politics. Despite increased scholarly focus on algorithmic governance, smart cities and their data production still heavily rely on human labor, raising questions about how that labor is recruited and the implications of different recruitment strategies. In this paper, we illuminate the relations and practices mobilized to recruit the labor required to produce, analyze, and enact data that (re)produce smart cities. We argue that smart cities recruit such digital labor by producing and circulating moral values and sentiments to claim that such participation is a social good. In this article we draw on a 6-year ongoing project in Calgary, Canada to explore how these “moral economies” underwrite smart city ecosystems. We explore three projects related to data and digital labor in the Calgary smart city: a wearable technology collaborative project, a civic hacking group, and the community social media platform Nextdoor. We suggest that moral economies of smart cities signal a new juncture between urban planning and profiting from data, with the potential for creating new socio-political risks. These moral economies signal a shift toward a “new spirit of capitalism” in which labor is managed through indirect persuasion rather than direct compulsion and mandate.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it