Bibliographic record
Abstract
People produce completely different noises when the cars stop: feet and words (Henri Lefebvre Rhythmanalysis, 2004, 28).It may not be on the summer festival schedule, but the manifestations casseroles are a daily popular music festival happening on the island of Montreal.Every night at 8pm, Montreal neighborhood residents pour out of their apartment doors and hang off their balconies with wooden spoons and saucepans in hand.In Villeray, my husband and I know when it is time to grab our instruments and join in the festivities by hearing the din of people passing by, joyously clanging on their cooking implements and other percussive, wind and brass instruments such as trumpets, congas and full drum kits (set up on balconies over looking the street), flutes, vuvuzelas, my orange maracas, and even a tuba.Drawing on powerful movement traditions that blend musical performance and protest -from civil rights and antiwar traditions to charivaris (for the latter, see Sterne and Zemon Davis, 2012) -les manifestations casseroles constitute music making en masse.It is an experience of acting together with our neighbors on a scale bigger than the block.Neighborhood by neighborhood, city wide, people are making music and moving in rhythm together to protest a law that aims to prevent unannounced collective action.Some manifs even incorporate dance, as seen in this youtube video, which highlights the rhythm and dance moves of manifestants (see around 1:40, in particular): "Montréal 28 Mai Manifestation des casseroles de Villeray avec rythme court vidéo."
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".