Silent Voices, Absent Bodies, and Quiet Methods: Revisiting the Processes and Outcomes of Personal Knowledge Production Through Body-Mapping Methodologies Among Indigenous Youth
Bibliographic record
Abstract
At the interface of Western and Indigenous research methodologies, this paper revisits the place of the “personal” and “autobiographical” self in qualitative visual research. We outline a community and partnership-based evaluation of a theater program for Indigenous youth using arts-based body-mapping approaches in Saskatoon, Canada, and explore the methodological limitations of the narrator or artist’s voice and representations to translate personal visual-narratives and personal knowledges they hold. In so doing, we describe how body-mapping methods were adapted and improvised to respond to the silent voices and absent bodies within personal visual-narratives with an epistemological eclecticism handling the limitations of voice and meaningfully engaging the potentiality of quietness. Extending the conceptual and methodological boundaries of the “personal” and “autobiographical” for both narrator and interlocutor, artist and observer, we contribute to debates on the processes and outcomes of personal knowledge production by articulating a generative, ethical, and culturally-grounded project mobilizing body-mapping as a quiet method that pursues self-work—the passionate and emergent practices of working on one’s self and making self appear in non-representational and ceremonial ways.
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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 | 0.035 | 0.038 |
| 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 | 0.030 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".