“Are you Navajo or Inuit?” Identity, television dialogue, and Indigenizing semiotics
Bibliographic record
Abstract
Abstract This study analyzes Indigenizing semiotic tactics in television narratives from the United States, combining corpus linguistic methodology with a theoretical framing inspired by linguistic anthropology. Given recent changes in the US television landscape, we analyze two landmark series with First Nations showrunners: Reservation Dogs and Rutherford Falls . Specifically, our dataset consists of all dialogue transcribed from both series' first two seasons. We use generic (e.g., Native , Indian , and tribe ) and specific (e.g., Navajo , Lakota , and Oglala ) identity labels as a starting point, combining corpus linguistic analysis of these labels with a semiotic analysis of selected scenes. The study identifies not only what identity work is being done by such labels but also how they are leveraged in the creation of an Indigenizing semiotics that disrupts “White” settler colonial frameworks that have traditionally been promoted in the media, enacting semiotic processes that we call overlay , icon‐marking , and erasure‐marking . A comparison with supplementary data from Australia allows us to show that these Indigenizing tactics are not limited to one country. Finally, the study demonstrates how a semiotic analysis of identity labels is a useful way “into” a larger corpus.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".