‘Killing’ the True Story of First Nations: The Ethics of Constructing a Culture Apart
Bibliographic record
Abstract
Cases taken from the coverage of Canadian/Ipperwash and American/Makah disputes over tribal land and sea claims point up that subtle but entrenched racist assumptions, conclusions, and myths of native culture persist despite attempts by newsrooms to be more culturally sensitive. Traditional journalism standards of practice and ethical approaches must be expanded to consider more of the subtleties of media's problematic representations of aboriginal peoples—as a culture, a culture apart, and a cultural construct. The ethics of continental philosopher Emmanuel Levinas, the ritual model of communication, and frameworks and methodologies used by feminist and cultural studies scholars are applied to show that journalism's current standards, which are rooted in Enlightenment ethics and embrace a transmission view of communication, are inadequate to the challenge of reporting on diversity in an ethnically complex world.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.126 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".