Bibliographic record
Abstract
This chapter outlines various tactics that artists, filmmakers, curators, architects, city planners, and arts administrators can employ to develop works, sites, and audiences that support a more participatory and representative public culture through massive media. These include: the application of analytical tools from cinema studies, namely superimposition, montage, and apparatus/dispositif, high-level coordination and provision of technical support from curatorial groups that see themselves as public space activists and community facilitators, and sensitivity towards context, both digital and virtual, of large-scale public data visualisations centred upon led façades. More study and practice is needed as the technologies and contexts of massive media shift and merge with the practices of digital placemaking and smart cities. Keywords: smart cities, digital placemaking, architecture, curation, cinema Dancing with Buildings You put your left foot in, you take your left foot out. And yes, you shake it all about as prompted by the screen in front of you. Soon enough your recorded image is projected for all of the people gathered around the Place Des Arts metro station in downtown Montreal who seem to be performing their own dance of spectatorship, watching intently or distractedly, snapping photos to post online, and cueing up for their turn. If you did any of these things, you would have just participated in McLarena (2014), the interactive public artwork by the Montreal design firm Daily tous les jours that I described in Chapter 2, a work that pays homage to Canadian film pioneer Norman McLaren's work Cannon (1964) by teaching people to dance like the character in his film and displaying the results on the side of a building for everyone to see. As buildings become more like screens with the addition of projection, LEDS, or built-in screen elements, we will increasingly find ourselves amongst public artworks and experiences like this in our cities — works that are large-scale, public, networked, interactive, participatory, and digital. In fact, McLarena shows us that what we now expect from public space, and what it can potentially deliver, is rapidly changing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.032 |
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".