Told-to Adaptations: Rabbit-Proof Fence , Whale Rider and The Lesser Blessed
Bibliographic record
Abstract
This chapter conceptualises the ‘told-to adaptation,’ in which non-Indigenous filmmakers adapt authors is adapted the work of Indigenous writers, as in Rabbit-Proof Fence(2002), adapted by Australian director Phillip Noyce from Mardudjara writer Doris Pilkington/Nugi Garimara’s Stolen Generations memoir, Follow the Rabbit-Proof Fence; Whale Rider (2002), adapted by Pākehā director Niki Caro from the Māori writer Witi Ihimaera’s novel The Whale Rider; and The Lesser Blessed (2012), adapted by Ukrainian-Canadian director Anita Doron from the novel by Tłı̨chǫ (Dogrib) author Richard Van Camp. Rabbit-Proof Fence adapts the narrative of Doris Pilkington Garimara’s mother, aunt, and their cousin’s abduction by authorities from their community, taken to the Moore River Native Settlement from whence they escaped, returning home along the longest fence in the world. Whereas Ihimaera’s The Whale Rider retells a Māori Traditional Story, most of the adaptation’s crew was Pākehā; further, the film is a New Zealand-Germany coproduction, diverging, sometimes sharply, from Ihimaera’s unused screenplays. The Lesser Blessed narrates a Dogrib teenager’s experiences in the Northwest Territories in the aftermath of his abusive father’s traumatic death. The novel’s contextualisation of this violence through the legacy of Canada’s Indian Residential Schools does not appear in the film.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".