Bibliographic record
Abstract
As practices in reactive architecture and locative media converge and urban screens and projection technologies proliferate we are becoming increasingly able to interact with data in public space. This confluence presents us with modes of digitally mediated participation in urban space that highlight bodily and architectural relationships with data rich environments as well as new sets of problems and possibilities regarding aesthetics, poetics, and politics. The article will analyze works by Alfredo Jaar, Krzysztof Wodiczko, and Rafael Lozano-Hemmer, as they respectively exemplify the efficacy of the key components of public data visualization: mapping, expanded presence through architecture, and the ‘incompleteness’ and participatory nature of relational aesthetics. A more recent example, the E-TOWER project, an interactive data visualization project of Toronto’s energy visualized on the CN Tower for Nuit Blanche 2010, will also be examined as a form of collective participation in public data visualization. These projects provide the case studies necessary to reflect on the concept of the public, the potential of relational art strategies and the utility of play strategies for combining visualization and public space in order to enrich these spaces through the dramatization, problematization, animation, and relation of people, places, and data with from-a-distance interaction and urban screens. Note: At the time of writing, Dave Colangelo was affiliated with Ryerson University and OCAD University.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".