Taking the Measure of Nuit Blanche 2014
Bibliographic record
Abstract
Abstract: Six years ago, Laura Levin and Kim Solga reflected in CTR on their travels through the 2008 edition of Toronto’s Scotiabank Nuit Blanche all-night art festival. That review offered a trenchant critique of the ways in which “the event that is Nuit Blanche” was beginning uncomfortably to “eclips[e] the art that is Nuit Blanche.” Today, with nearly a decade of Nuit Blanches in Toronto behind us, with attendance numbers increasing every year, and with Toronto an established “creative city” at the forefront of urban neoliberalism, that critique seems almost too obvious. Nuit Blanche is now marketed and represented in the media as a good-time street party, but it does not necessarily follow that the meanings Nuit Blanche makes for spectators on the ground are in any way stable, or necessarily banal. In October 2014, Solga set out for Nuit Blanche with a vanload of students from her undergraduate performance studies seminar. The goal: to experience as much of the festival as possible, with an eye to investigating the ways in which the art on offer hailed it spectators and invited active, participatory engagement. What did Nuit Blanche want from a keen and largely novice group of performance art enthusiasts? What would it offer in return? Would it let them down? And, if so, what might they learn from their disappointment?
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".