Low-Resolution Media Façades in a Data Society
Bibliographic record
Abstract
The highly visible and data-reactive low-resolution displays of buildings like Toronto's CN Tower or New York's Empire State Building shape the texture, tempo, and legibility of the urban experience, an experience that is produced (and consumed) in a unique combination of on and offline activity. I argue that these expressive surfaces increase the ambivalence and contingency of the ways we read (and write) the city, enabling the formation of temporary publics through public data visualisations that combine elements of democratised urbanism, critical debate, emotion, control, and commerce. Through historical research, social media analysis, and research-creation, this chapter focuses on the specific case of the Empire State Building and reports on the relationships between information, public space, and architecture that are sustained and supported by low-resolution, expressive architectural façades. The chapter ends with a discussion of the potential for artistic and activist uses of low-resolution digital architectural displays. Keywords: media façades, media architecture, social media, digital culture, information aesthetics This Building is on Fire It certainly was not the first time New Yorkers saw flashing lights atop the Empire State Building; but, it was the first time the lights danced as they did, synchronised to Alicia Keys’ singing two of her songs, ‘Girl on Fire’ and ‘Empire State of Mind’, appropriately selected for the launch of the building's newly installed programmable LED lighting system. Amidst the ambient glow of the surrounding buildings, bright orange and red hues shifted to blue, purple, and yellow with the pulsating rhythm of the music. The colours mixed and faded into one another, rippling across the façade and rising up and down the antennae to Keys’ voice. As Megan Garber (2012) of The Atlantic described it, it was like ‘a fireworks show, with the illumination in question coming not from controlled explosions, but from controlled LEDS’. It was a firework-like show that in its apparent silence could be completely ignored or misinterpreted by thousands while remaining a formidable centre of attention for those who knew what to look and listen for by tuning in to the synchronised audio on a local radio station. Furthermore, the show rippled out into the night (and the days thereafter) on screens of those near and far via YouTube (see Figure 3-1), Instagram, and Twitter, and Facebook.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".