Bibliographic record
Abstract
Distributed by Film MovementProduced by Emile Hertling Péronard, Alethea Arnaquq-Baril, Stacey Aglok MacDonald, and Bob MooreDirected by Lin Alluna2023, Streaming, 92 mins At a young age, Aaju Peter left her native land of Iqaluit, Canada to pursue studies in Denmark. As an Inuit in a foreign land, she experienced first-hand the colonial mindset and judgements of people around her. Throughout this film, Aaju expresses the frustration and confusion of meeting modern day society’s expectations and prejudices of indigenous peoples. Aaju Peter grew to be a lawyer who now fights for indigenous rights. Still to this day, Iniut peoples are fighting just to have a say in what happens to their native land. From overfishing and seal hunting by outsiders, the Inuit people still struggle to survive in a modern world that often overlooks indigenous populations. This documentary is an essential companion to understanding the devastating effects of colonialism on indigenous people. While we often discuss the past in relation to the mistreatment of native people, we tend to overlook the mistreatment continuing today. By providing Aaju Peter a platform to discuss the dark past and present, director Lin Alluna is giving the Inuit people a chance to share their side of history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.107 | 0.014 |
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".