Bibliographic record
Abstract
This paper shares one of the critical moments in an interdisciplinary collaboration between an urban geographer/planner and a visual artist in which we explore different ways of seeing, knowing, mapping, and imagining. Our work develops integrated and participatory spaces in which to generate stronger and more nuanced geographical and artistic insights into people’s embodied experiences and encounters with/of urban space. This essay shares the example of a playful data intervention conducted by students prompted to engage in the complexities and possibilities of digital landscapes. It looks at urban surveillance as a technological ecosystem, thinking particularly about traffic cameras, weather cameras, and other visual monitoring systems—digital infrastructures premised on gathering “data.” It also thinks about poetic and experiential alternatives to this way of conceptualizing space. The camera is probably the first step toward integrated urban technological living—from which we can extrapolate and research what other kinds of things are being implemented and how that might fit with the (un)availability of user experience. In response, we proposed a participatory project engaging the question of urban citizenship in which participants find themselves inside of this visual ecosystem and share pictures of themselves taken from publicly available surveillance cameras. We call it “smart city photo booths” and our hope is that it helps us rethink the relationship between data and lived experiences within digitally mediated society. It gets into the concept of “smart” data. We think about smartness as a form of research/espionage that perhaps requires citizen participation and collective human (counter) intelligence to the data-imperatives emerging in the discourses around smart cities.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".