Bibliographic record
Abstract
To provide the necessary context for this chapter, I will first introduce my qualitative research on the performative power of vocality which has been significantly informed by the project “Honoring Cultural Diversity through Collective Vocal Practice,” funded by the Social Sciences and Humanities Research Council of Canada. This collaborative arts-based inquiry initiative consisted of a series of cross-cultural and intergenerational community gatherings and workshops co-facilitated by myself, four graduate students, and the seven members of the project&s;s Indigenous Advisory Committee. We also co-authored a text braiding our twelve voices together and created a companion documentary film for the book Research and Reconciliation: Unsettling Ways of Knowing through Indigenous Relationships , edited by Shawn Wilson, Andrea Breen, and Lindsay DuPré ( Aluli-Meyer et al., 2019 ). This collective testimony of our experience of resonance as a practice of ritual engagement—the title of our plurivocal chapter and film—conveys our search for echoes and resonances while leaving space for dissonances. It unmoors individualist conceptions of creative agency and reflects the challenges of working toward reconciliation, pointing to something other than unanimity, unison or perfect harmony. I discuss this embodied research in my monograph The Performative Power of Vocality ( Magnat, 2019 ), which offers an interdisciplinary and cross-cultural approach to the non-discursive, material, affective efficacy of vocality, beyond notions of voice as a conceptual abstraction or a metaphor, and beyond its association with speech and language, making vocality exclusively human, hence problematically anthropocentric.
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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 | 0.073 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.146 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".