Bibliographic record
Abstract
The city is a network of boulevards, thoroughfares, highways and subway systems. The city is also a site of learning, or rather the city houses sites of learning: museums, libraries, schools, theatres and cinemas, for instance. Interestingly, these sites of learning need not be physical; indeed, regarding cinema, for example, they can be websites like Amazon, Criterion Channel, Netflix, where cinephiles can stream movies from the comfort of their home, office, car, on a phone, tablet, television. It is at the intersection of these sites – the cinema and the city – that I wish to situate this article. In particular, I explore how the filmmaker Guy Maddin with My Winnipeg () finagles cinema’s essay-film genre to turn a physical space (in Winnipeg, Manitoba, Canada) from, in Deleuze and Guattari’s terms, striated to smooth, points to pointillism, in which event replaces essence and multipli-city replaces singular(c)ity. The city is a machine and the machine here is D+G’s assemblage. As such, the city and the citizen/creator become one, symbio[(y)tic], and the two cannot be separated without returning the city to a simpli-city and the filmmaker to a documentarian. This film amounts to an encounter that causes thinking (in Deleuzian terms) and thus learning and thus a way forward for thinking through a pedagogy of the permanent circuit.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".