Fostering Reconciliation through Collaborative Research in Unama’ki: Engaging Communities through Indigenous Methodologies and Research-Creation
Bibliographic record
Abstract
Abstract This article documents relationships, strategies, and activities involved in developing and carrying out collaborative community-engaged research for reconciliation, based on Indigenous methodologies and research-creation. It documents an example of Indigenous/non-Indigenous collaboration in Unama’ki (also known as Cape Breton, Canada), providing data towards the refinement of models of research designed to foster reconciliation, and contributing to a literature on Indigenous/non-Indigenous collaborations in ethnomusicology and related fields. While revealing some challenges in the process with respect to addressing local needs, it also describes transformations that can be achieved through effective collaboration, including ways in which universities can be involved.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.036 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| 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".