‘Songs You Need to Hear’: Public radio partnerships and the mobility of national music
Bibliographic record
Abstract
Abstract Public radio broadcasters are mandated to act as vehicles for supporting and promoting national culture, including music. Despite a predominately national focus, the Canadian Broadcasting Corporation has partnered with public broadcasters from other nations in a song-sharing initiative called ‘Songs You Need to Hear’. The initiative includes a monthly blog post with embedded audio and brief descriptions of the music by radio hosts from CBC (Canada), BBC (UK), NPR (US), ABC (Australia), and RTÉ (Ireland). This article explores the ways in which a mobile, transnational song-sharing project emerged between 2000 and 2015 and what it reveals about the pressures and new models developed in this period of digital transmission. ‘Songs You Need To Hear’ represents the current state of public media in which the need to digitize, globalize, and universalize, combined with unreliable funding models, has resulted in the treatment of music on the radio as inexpensive and highly accessible content that straddles the line between the global brand extension of public media institutions and ideas about the fundamental role of public media in their support of national culture.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".