Reflecting Aboriginality : informing the development of a terminology guide for journalists
Bibliographic record
Abstract
This study draws upon qualitative interviews with journalists working within organizations that are members of the Strategic Alliance of Broadcasters for Aboriginal Reflection (SABAR), including, but not limited to, Aboriginal Peoples Television Network, CBC Television, and OMNI Diversity Television. This study finds that a terminology guide for Aboriginal reporting is a necessary and long overdue journalistic resource. This thesis also finds that an online guide is the most accessible method of delivery for journalists. The study’s third key finding provides an indication of what journalists think SABAR’s guide should contain in order to improve coverage of Aboriginal communities. Thus, SABAR’s guide is important because it will offer journalists a way to be more accurate in their portrayals of Aboriginal people. SABAR’s guide represents a significant—and unprecedented—step toward informing accuracy in Aboriginal reporting.
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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 | 0.138 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".