Bibliographic record
Abstract
QR (Quick Response) codes have become ubiquitous graphic artifacts within our post-pandemic cityscapes, mediating a great variety of activities and reconfiguring routine practices and daily interactions. Drawing on a series of ethnographic observations on the use and display of QR codes collected in different global cities (Milan, New York, Jakarta, Toronto), this article explores the ideological and semiotic infrastructures underlying the contemporary trope of the ‘Smart City’ and the role of digitally encoded data in organizing information and conduct in our post-pandemic present. It is argued that the new machine-readable data encoding standard called QR code lies at the heart of different representations of the future metropolis. Suspended between dystopian visions of digital surveillance and techno-optimist fantasies of cyber-metropolitan lifestyles, the actual encounters with QR code-mediated infrastructures are, in fact, the theatre of a new post-pandemic techno-corporeal regime wherein new forms of ideological and sensorimotor compliance are interspersed with unintended glitches and artful acts of defiance. The article discusses the interplay between acts of ideological and corporeal alignment with the contemporary conceptual construct of the ‘Smart City’ and the emerging infrapolitics of alternative digital textualities. In so doing, it describes, how in the post-pandemic world, QR codes are embedded in a complex history of remediation and repurposing. At once infrastructures of consumer desire and statecraft surveillance, they generally interpellate users in ways that conflate the role of citizen and consumer. The analysis, however, shows how QR codes can be openly contested or occasionally “remediated” and used to unsettle expected outcomes, redirecting users to innovative forms of political action and aesthetic intervention.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".