Coming to you wherever you are: MuchMusic, MTV, and youth identities
Bibliographic record
Abstract
Networks is the undisputed international music video gatekeeper, with stations from Australia to India, Russia to Brazil. is one of the few countries to resist its global reach. Although the network has launched MTV Canada with an affiliate, that station limits its offerings primarily to talk shows and lifestyle programming. Many Canadians regard the Toronto-based MuchMusic as the nation's important domestic source of music videos-substantially different from, and superior to, American-based MTV. In her new study of the two music channels and their different cultures, Kip Pegley compares the musical and extra-musical content of MuchMusic and MTV, and examines how the stations construct their two distinct identities. Moving beyond analysis of individual videos, Pegley looks at the overall programming of each station, uncovers the well-hidden matrixes of power that dictate both which performers appear and what genres get the most airtime, and delves into how ideas of gender and race serve to naturalize distinct and complex nationalist ideologies. In so doing, she discovers why Canadians feel so protective of their music video station, and why they successfully have withstood the invasion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".