Funding Nonprofit Radio Technology Initiatives in Canada
Bibliographic record
Abstract
Media organizations worldwide are struggling to find sustainable financial models since the arrival of the internet. Nonprofit radio is no different. Using a thematic analysis of 62 Canadian nonprofit stations’ financial statements from 2012–2015, this study examines the impact of the Community Radio Fund of Canada’s Radiometers’ grant competition. Although results show a small financial gain for those who received funding, the study fails to determine the value of relying on such a grant for long-term technological sustainability. This study also shows the classic income effect by demonstrating how stations continued spending on technology whether they received grants or not. Recommendations include creating a matching fund program to encourage stations to find alternative sources of income to sustain their projects and increase accountability.Les organisations de médias à travers le monde luttent pour trouver des modèles financiers durables depuis l’arrivée d’internet. La radio à but non lucratif n’y échappe pas non plus. En effectuant une analyse thématique des états financiers de 62 stations canadiennes à but non lucratif de 2012-2015, cette étude examine l’impact de la compétition Radiomètres du Fonds canadien de la radio communautaire. Malgré le fait que les résultats démontrent un petit gain financier pour ceux ayant reçu du financement, l’étude ne parvient pas à démontrer la valeur de ce type de subvention pour une durabilité technologique à long terme. Cette étude valide aussi l’effet de revenu classique en démontrant que les stations continuent à effectuer des dépenses en technologie, peu importe s’ils ont obtenu ou non une subvention. Les recommandations comprennent la création d’un programme de fonds de contrepartie, afin d’encourager les stations à trouver des sources alternatives de revenus afin de soutenir leurs projets et d’accroître l’imputabilité.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".