Bibliographic record
Abstract
Abstract In this chapter, the author turns to Diné concepts and overall Indigenous community features (language, accessories, and epistemologies) to demonstrate that Indigenous televisual stories are restorative and evocative of Indigi-realism through Indigenous “ ‘Aye!’sthetics” as storytelling autonomy. “Aye!”sthetics fuses the Turtle Island Indigenous utterance “Aye!” that expresses joking and joy with the beauty and autonomy of on-screen presence and Indigenous existence. Two Indigenous-centered television shows, Reservation Dogs and Rutherford Falls, showcase Indigenous aesthetics, or what I explore as “Aye!”sthetics that offer a glimpse of Indigi-realism in visual storytelling. The author introduces Diné (Navajo) epistemologies as a framework to interrogate Indigenous realities on screen. By focusing on select scenes from the combined thirty-six episodes of each series, the author highlights the beauty, laughter, and resilience of Indigenous peoples, eclipsing heartbreak and adversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".