Bibliographic record
Abstract
Distributed by Good DocsProduced by Jadis Dumas and Amy MillerDirected by Jadis Dumas2019, Streaming, 72 mins Another Word for Learning follows the scholastic journey of 11 year old Aisha O’Sullivan a Kwakwaka’wakw girl who is choosing how she wants to be educated while living in Vancouver’s Downtown Eastside. Her mother, activist Gunargie O’Sullivan, who is a local radio personality, helps guide Aisha through her journey. Aisha is a bright and talkative tween who feels ignored in the traditional school setting. Aisha has ambitions to discover other alternative ways of learning while affirming her Indigenous sense of identity and understanding. At one point in the movie, she talks about how school for her would work better if they taught more hands-on skills and art other than just science, languages, etc. Moreso, not only following the mother-daughter duo, Another Word for Learning examines the disconnect between the contemporary colonial educational system and Indigenous communities, considering Canada’s Truth and Reconciliation Commission (TRC). Canada’s Truth and Reconciliation Commission was officially established on June 1, 2008, with the purpose of recording the history and lasting effects of the Canadian Indian residential school system on Indigenous students and their families. It offered residential school survivors an opportunity to share their experiences during public and private meetings held across the country. “The TRC emphasizes that it has a priority of displaying the impacts of the residential schools to the Canadians who have been kept in the dark from these matters.” (“Truth and Reconciliation Commission of Canada.” Wikipedia. Wikipedia.org, 31, Oct, 2022, https://en.wikipedia.org/wiki/Truth_and_Reconciliation_Commission_of_Canada#cite_note-5) This film would be a useful addition to collections supporting coursework in Native American Studies or Indigenous Peoples as well as education programs looking at alternative educational models. It would also be suitable for younger audiences studying Native American cultures. Awards:Best Cinematography, Dada Saheb Phalke Film Festival; Official Competition, Local Sightings Film Festival; Official Competition, DOXA Documentary Film Festival; Official Competition, Garifuna International Festival; Official Competition, RIDM
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.739 | 0.575 |
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".