Documenting Oral History and Lessons in Truth Telling in in Nadia McLaren’s <i>Muffins for Granny</i> and Tim Wolochatiuk’s <i>We Were Children</i>
Bibliographic record
Abstract
While fictional and non-fictional writing on Indian Residential Schools (IRS) has become an important part of the academic landscape well beyond the confines of Canada, documentary filmmaking on IRS has not yet been met with the same level of scholarly attention. This essay on Nadia McLaren’s Muffins for Granny: Stories from Survivors of the Canadian Residential School System and Tim Wolochatiuk’s We Were Children seeks to reduce this divide. As a powerful form of truth telling, these documentaries testify to the power of oral history on par with indigenous storytelling practices and oral traditions, but they take highly different approaches to the sharing of the testimony of residential school survivors and their traumatic memories. McLaren artfully fuses the participatory mode of documentary filmmaking with the balance and harmony of an Aboriginal worldview. Wolochatiuk takes a more controversial approach, stretching the borders between fact and fiction with his highly affective brand of performative documentary filmmaking.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.038 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".