Media as Constructor of Ethnic Minority Identity: A Native American Case Study
Bibliographic record
Abstract
Media representations and concepts of ethnic minorities on those representations become important if one tries to understand the media’s role as constructor of ethnic self-identity. Native Americans provide a classic example—synonymous here with “American Indian.” In Canada, the term “First Nations” is often used (Iverson, 1998, p. 4) along with “indigenous,” when referring to indigenous people of the world (cf. Alia, 1999). Their media representations have been quite one-sided and uniform, imitating the model of the nineteenth century Plains Indians as created by Hollywood (Hilger, 1995; Churchill, 1998; Kilpatrick, 1999; Bataille, 2001; Pearson, 2001; Rollins and O’Connor, 2003; Aleiss, 2005). Elizabeth Bird (1999) discusses how representations of American Indians are structured in predictable, gendered ways: Women are faceless, rather sexless squaws in minor roles, or sexy exotic princesses or maidens who desire white men. Men are either handsome young warriors, or safe, sexless wise elders. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".