Bibliographic record
Abstract
La proposition sur laquelle je fais reposer mon analyse est que le rap montréalais est plus qu’un genre musical, mais un lieu qui est le résultat changeant de l’intersection de forces, d’interactions sociales, ce que Doreen Massey (1993 et 2005) appelle des «trajectoires». Pour elle, un lieu se construit continuellement, à la fois dans le temps et dans l’espace, par l’interaction simultanée, à plusieurs niveaux, de ces «stories-so-far» (Massey, 2005). Mon objectif est de circonscrire quelques-unes des «trajectoires» qui composent le rap à Montréal, et d’identifier celles quisemblent être les plus pertinentes. Pour ce faire, j’ai effectué une filature de trois groupes/artistes faisant du rap à Montréal. En suivant ces artistes dans leurs activités professionnelles sur une période de quatre mois, je suis entré en contact avec des acteurs humains (journalistes,programmateurs de festivals, agents d’artistes, fans) et non-humains (salles de spectacles, studio, locaux de répétition, boutiques restaurants, stations de métro). Ce sont leurs interactions qui forment les trajectoires qui recomposent continuellement le rap comme lieu.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".