Bibliographic record
Abstract
In aesthetic inquiry, the sublime has remained a versatile signifier used to theorize art movements across the ages in a wide variety of artistic media. Highly influential in literature and visual arts, it has adapted to the sensibilities of changing times and objects of study. A new understanding of the concept has emerged today: the data sublime. From data visualizations to mass surveillance, the moniker data sublime has been used in the past decade to refer to the abstract manifestations of information technology in the everyday. This expression of sublimity foregrounds data as a form that is virtual, transcendent and beyond the reach of the sense apparatus. Yet data as an artifact is indelibly written and recorded by human hands. What urban representations might this paradox render possible? Are dataspaces myths or the experience of real places? This essay speculates on some of the relationships that might exist between data sublimity and placemaking in design research. It contributes to the literature by engaging in a reflection on how the data sublime could be recast and applied to media architecture. Several case studies are presented to ground this discussion and suggest potential avenues for future research.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".