Bibliographic record
Abstract
À partir des conclusions tirées de notre thèse (consacrée à l’étude du phénomène de l’enregistrement en studio de la paranda garifuna en Amérique centrale), cet article soulève certaines des préoccupations théoriques et méthodologiques qui apparaissent quand l’ethnomusicologue contemporain décide de faire du studio d’enregistrement son principal terrain de recherche. Dans ces studios où se créent les musiques à vendre, des relations de pouvoir se déploient en permanence autour d’un enjeu principal : le contrôle de la manipulation électronique des sons enregistrés. S’il fait office de laboratoire expérimental pour ses acteurs (musiciens, ingénieurs du son, réalisateurs…), le studio d’enregistrement offre à la recherche ethnomusicologique un microcosme au sein duquel un appareillage technique sophistiqué se trouve manipulé en fonction d’interactions entre des individus. En se basant sur une revue de littérature interdisciplinaire, cet article propose une base théorique et méthodologique pour toute recherche ethnomusicologique portant sur le rôle et la place du studio d’enregistrement dans l’analyse de phénomènes musicaux contemporains.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".