When you sing it now, just like new: First Nations poetics, voices, and representations
Bibliographic record
Abstract
Part One: ActualitiesIntroduction to Part One1. Memories and Reflections: Learning from Dane-zaa Women - Jillian Ridington 2. Hunting for Stories in Sound: Sharing Ethnographic Authority - Jillian Ridington and Robin Ridington 3. Soundman: A Guide to Field Broomfield and Stories from the Field - Jillian Ridington and Robin Ridington 4. Keynotes from the Dane-zaa: A Guide to In Doig People's Ears - Robin Ridington 5. Why Baby Why: A Guide to Howard Broomfield's Documentation of the Dane-zaa Soundscape - Jillian Ridington and Robin Ridington 6. Maintaining Dane-zaa Identity: Those Story I Remember, That's What I Live By Now - Jillian Ridington and Robin Ridington 7. Contact the People: A Guide to a Video on Dane-zaa Change and Continuity - Jillian Ridington and Robin RidingtonPart Two: Poetics and Narrative TechnologyIntroduction to Part Two8. Models of the Universe: Musings on the Language of Benjamin Lee Whorf - Robin Ridington 9. Voice, Representation, and Dialogue: The Poetics of Native American Spiritual Traditions - Robin Ridington 10. That Is How They Grab It: Celestial Discourse in Dane-zaa Music and Dance - Robin Ridington 11. Dogs, Snares, and Cartridge Belts: The Poetics of a Northern Athapaskan Narrative Technology - Robin Ridington 12. Tools in the Mind: Northern Athapaskan Ecology, Religion, and Technology - Robin RidingtonPart Three: Re-Creation in First Nations LiteraturesIntroduction to Part Three13. You Think It's a Stump but That's My Grandfather: Narratives of Transformation in Northern North America - Robin Ridington 14. Fieldwork in Courtroom 53: A Witness to Delgamuukw v. A.G. - Robin Ridington 15. Theorizing Coyote's Cannon: Sharing Stories with Thomas King - Robin Ridington 16. Happy Trails to You: Contexted Discourse and Indian Removals in Thomas King's Truth and Bright Water - Robin RidingtonEpilogue
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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 | 0.002 | 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 | 0.010 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".