Bibliographic record
Abstract
“You are our tomorrow,” is an empowering statement that January Spears (Michelle Thrush, Cree) says to her on-screen daughter, Aline (Grace Dove, Secwépemc) in the recent theatrical release of Bones of Crows (2022) by Dene/Métis auteur Marie Clements. For the 10th Anniversary Issue of Transmotion, I propose to write about the film, using a Diné-lens and analytic to argue that visual storytelling directly benefits the lives of Indigenous peoples and communities. Bones of Crows is the most recent feature-length historical-fiction film about Indian Residential Schools (IRS), and its ongoing effects (Turtle Island “education” is known as residential schools north of the Medicine Line and as boarding schools, south of the Medicine Line). The movie extrapolates almost a century of Indigenous-centred resistance to genocidal legislation (including the starvation policy and the Indian Act) through heartwarming and heartbreaking lived experiences that transcend borders. Clements’s film showcases a Cree-speaking survivor, from her childhood through adulthood to Elderhood. Aline’s life story epitomises thrivance, which according to the journal by the same name, is “Indigenous ways of being, knowing, and doing.” Though I pay homage to key conversations in critical Indigenous film studies, I expand upon my recent deployment of a Diné analytic, which is grounded in Diné language and philosophy. The Dene and Diné are linguistic relatives, yet our kinship ties were severed over time. However, using thrivance to ground the work, I will demonstrate how—despite ongoing adversity— the daily tenants of striving to live a life of wellness and balance (as taught to contemporary Dene and Diné) intersect with onscreen Indigenous presence which culminates in a moving and beautiful rendering of restoration: both personal and communal. Clements’ Indigenous film aesthetics highlight music, languages, and resilience, which exemplify Dene storytelling autonomy to reflect vibrant Indigenous tomorrows.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".