Urban Media Studies| Practicing Urban Media Studies: An Interview With Will Straw
Bibliographic record
Abstract
In this interview, Simone Tosoni and Seija Ridell discuss with Will Straw, professor of urban media studies at McGill University, Canada, his views of this subject area. Professorships that would explicitly focus on the intersection of media studies and urban studies are rare internationally, Straw’s position being one of them. The interview sheds light on how urban media studies came about and were institutionalized at McGill University, how Straw practices urban media studies in his own teaching, and how he sees the future of this “interdiscipline.” The second part of the interview addresses two of Straw’s main research topics and their relation to urban media studies: his studies on scenes and the night. Will Straw is James McGill Professor of Urban Media Studies in the Department of Art History and Communications Studies at McGill University. He is the author of Cyanide and Sin: Visualizing Crime in ’50s America (Andrew Roth Gallery, 2006) and coeditor of several volumes including Circulation and the City: Essays on Urban Culture (with Alexandra Boutros), Formes Urbaines (with Anouk Bélanger and Annie Gérin), and The Oxford Handbook to Canadian Cinema (with Janine Marchessault). He has published widely on popular culture of all kinds and is the author of more than 150 articles on music, cinema, and urban culture.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".