Bibliographic record
Abstract
Urban technologies are increasingly designed to support ubiquitous computing, which now includes different forms of digitally-augmented interactions in public space. This shift is underpinned by the development and management of digital infrastructures in metropolitan cities – a paradigm often rhetorically dubbed ‘smart cities’. Because the cityscape is uneven and characterized by diversity, this reconfiguration could be seen as a welcome opportunity to renegotiate the issue of agency in relation to the new technologies embedded in the built environment. Since the Urban Screen project was launched in 2005, digital art installations commissioned for public space have offered propitious terrain for rethinking this issue. Developing appropriate research methodologies, which could better support democratic practices within the infrastructural approach to urban technology design still stands out as pressing and necessary to facilitate the engagement of all concerned. This essay argues in favour of multidimensional approaches over unidimensional ones. To ground this discussion, it first describes the results of a unidimensional study carried out in 2015 in Montréal’s Quartier des Spectacles and then highlights some of the salient differences it presents with a multi-sited field study conducted on the same site from 2012-15. It finally concludes that a multidimensional approach seems more robust.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".