Dutcher's Impact: Wolastioqiyik Lintuwakonawa as a Case Study for the role of Music in Preserving Traditional Knowledge
Bibliographic record
Abstract
Knowledge Management is a diverse field of study, dealing in the facilitation of knowledge sharing, the creation of knowledge systems, knowledge transfer, and knowledge preservation. Information professionals play an important role in helping these processes happen. Equally important is the preservation of Traditional Knowledge. Recognized as the knowledge Indigenous people have accrued over millennia, and formed through their interactions with their environment, Traditional Knowledge and its preservation also fall into the world of Knowledge Management. The performance of a piece of music is the manifestation of knowledge and, in the case of Jeremy Dutcher, is a form of knowledge preservation. Traditional Knowledge’s more fluid and dynamic nature is preserved in Dutcher’s 2018 album Wolastioqiyik Lintuwakonawa, where the artist creates a conversation between technical skill and the knowledge and language of the album. In the case of this paper, Dutcher’s album serves as an example of the way Traditional Knowledge can impact and provide new tools to the information profession and world of Knowledge Management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".