Music 4 Cancer: Appreciation of an underground music festival and its philanthropic purpose among festival goers
Bibliographic record
Abstract
Music 4 Cancer (M4C) is an underground music festival that has taken place every September, in Sainte-Therese, Quebec, since 2010. One of its purposes is philanthropic: to raise funds for cancer research. This disease, responsible for 30% of deaths in Canada, is considered the deadliest and for which there is no effective treatment. M4C is one of five philanthropic festivals in Quebec, but the only one that combines music and cancer. This article presents the evaluation of the overall appreciation of the M4C festival, both of the underground music being played and its philanthropic purpose, as well as the motivations of the festival-goers for attending and returning (becoming loyal) to it. In order to better know and understand their appreciation, a double data collection was carried out. An online questionnaire collected quantitative data from 107 festival-goers in the fall of 2017, and a semi-structured interview was conducted with nine of them in the winter of 2018. Three strong points emerged from the results analysis. First, most of the festival-goers have a very high overall appreciation of M4C. Second, most of them have a very high appreciation of the underground music being played and its philanthropic purpose. Third, fundraising for cancer research is a big part of the motivation among most of the festival-goers for attending and returning (becoming loyal) to it.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".