Urban Cartographies: Mapping Mobility and Presence
Bibliographic record
Abstract
This special issue takes up new media in situ, addressing how new media technologies have the potential to re-orient us and, by extension, radically intervene in our understandings of place—specifically the public spaces of the city—and our place in it. We not only explore the specificities of these new media technologies and the cultural practices they afford but also highlight the intimate relationships they instantiate with their surroundings. The specific case studies highlighted in the contributors’ essays discuss gaming in Canada (Engel) and Japan (Hjorth), the traces of racism in South Carolina (Cooley), the topographical footprint of settler colonialism (Zwicker et al), Hong Kong pace (Wilmott), and artistic experiments that use the city as a laboratory (Verhoeff). What holds all of these contributions together is their indebtedness to creative cartography. This special issue on Urban Cartographies explores the paradoxes of presence, co-presence and absence as represented on and generated by our living, mediating screens.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".