Bibliographic record
Abstract
The growing critical research agenda on smart cities and open data programs has largely overlooked the body-subjects that enable its (re)production. The “ideal” subject of the smart city is prefigured as tech-savvy, independent, and uber-modern, able to produce digital data and analyze it to hold city government “accountable.” In this subject production, however, we argue that smart cities continue to rely on forms of reproductive labor that are invisibilized in current research and public discourse: We focus here on unpaid domestic labor, low-paid caring and reproductive labor, and volunteer work. We introduce the term “digital care worker” to capture a new category of reproductive worker in the smart city—voluntary and low-paid data producers and analyzers such as those who undertake “hackathons,” usually expected to do so out of love for their cities and communities. Drawing on geographies of care and Eve Sedgwick’s notion of the “closet.” we argue that the invisibility of digital caring laborers exists in dialectic relation to the spectacularization of particular body-subjects charged with caring for the smart city. Drawing on a discourse analysis of promotional materials and mission statements of key open data advocacy organizations, we propose the idea of “marginalized coder incubators,” who deploy assimilationist rhetoric to spectacularize the voluntary labor of women, people of color, and LGBTQ communities that is ultimately performed for the benefit of elites in the neoliberalizing city.
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".