Bibliographic record
Abstract
This article summarizes the research creation activities as part of Singing Storytellers in 2014, and subsequently the development of knowledge dissemination outputs through deep collaboration with scholars and artists. Over the past decade, the field of research creation has brought together academics and practitioners across institutes of higher education, the arts sector, and the creative industry in Canada and internationally. My work as a documentary filmmaker and sound artist has expanded to a curatorial practice that centers community engagement, in no small part due to the connections with artists and scholars, training opportunities, and mentorship from which I benefited during this project while I was a graduate student. While collaborations between the musicians and scholars involved in Singing Storytellers have thrived since and resulted in multiple initiatives, this article reflects on the initial collaboration that brought a novel approach to the Sunjata epic performance accompanied by poetic translation to the public through multimedia. Here, I reflect on the process of filming, editing, and presenting The Sunjata Story – Glimpse of a Mande Epic drawing on methods from visual anthropology and ethnomusicology. I conclude with a consideration of how acts of reciprocity can create more sustainable relationships between artists and scholars – a practice I now incorporate into my own scholarship today.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".