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Record W7025395397

“We paid a price to sing this music”: The American Recording Industry, Aging R&B Performers, and the 1984-2004 Royalty Reform Movement

2015· other· en· W7025395397 on OpenAlexfundaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2015
Typeother
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of WarwickUniversity of Ottawa
KeywordsBanquetCollectableWeb siteMovement (music)
DOInot available

Abstract

fetched live from OpenAlex

Au nom de l'exécutif d'IASPM-Canada (Jacqueline Warwick, présidente), du comité de programmation (présidé par Richard Sutherland) et du comité des arrangements locaux, nous sommes très heureux de vous accueillir au 32e colloque annuel d'IASPM-Canada, organisé par l'Université d'Ottawa et Carleton University.Le projet, financé par le programme Connexion du CRSH, est destiné à favoriser le dialogue interdisciplinaire à propos des intersections de la musique et des images, selon une gamme de formes culturelles variées, comprenant les vidéoclips, le cinéma et l'iconographie de la musique populaire.En mettant l'accent sur les intersections entre la vue et le son, ce colloque fera avancer les connaissances de domaines-clés: les technologies des nouveaux médias relatives au son et à la musique; les pratiques des médias sociaux; les images de marque et le développement de produits; l'esthétique musico-visuelle du nouveau millénaire; les représentations culturelles des genres, des races et des classes sociales; et des méthodologies historiques et théoriques pour l'étude du son et de l'image.Le programme riche et varié de cette année reflète la diversité et l'ampleur des études en musique populaire au Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.397
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.013
Scholarly communication0.0100.004
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.050
GPT teacher head0.289
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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