HOW DO ARTISTS INNOVATE ON SCENE? UNDERSTAND THE IMPLEMENTATION OF ARTISTIC INNOVATION THROUGH THREE CANADIAN MUSIC FESTIVALS.
Bibliographic record
Abstract
Implementing artistic innovation in music festivals is complex but also risky. Indeed, any failure in its realization can have a negative impact on the success of the festivals. In this study, the objective is to understand how music festival stakeholders conceive of artistic innovation to provide the tools and best practices to make it more successful. Based on a qualitative research, the analysis revealed that stakeholders in the achievement of artistic innovation in music festivals can be human or non-human actors. Secondly, regarding strategies for the implementation of artistic innovation, it appears that the realization of artistic innovation can take place in three forms. It can be the result of a combination of activities between several stakeholders in the form of collaboration. It can also arise from a controversial situation where several forces are in contradiction. In this case, it is from a situation of interaction that artistic innovation arises. Finally, artistic innovation can be only the result of an individual action of a given stakeholder. In this instance, it is the result of an individual activity carried out by a single actor.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".