Limited interests, resources, voices: power relations in mainstream news coverage of Indigenous policy in Australia
Bibliographic record
Abstract
Recognising the importance of who gets to speak in constructing knowledges about Indigenous peoples, this article examines power relations regarding mainstream news coverage of the Indigenous policy of Northern Territory Emergency Response (NTER) in Australia. Integrating content and discourse analysis of newspaper and television stories over a 3-year timeframe with interviews with journalists, this article found media coverage of the NTER, commonly known as the Intervention, followed a pattern of decline, with occasional peaks around events that were newsworthy through the lens of conventional news values. Further, analysis of three key discourse moments found ‘official’ discourses, particularly by the government, overpowered those of Indigenous peoples living under the policy. This article demonstrates how particular journalistic practices – news values, ideas of audiences, and use of sources – together with resource limitations and discursive practices of government provided dominant discursive power on the Intervention to government representatives. The article concludes that daily routines of news media and discursive practices of media savvy social actors perceived as ‘official’ or ‘expert’ by media professionals form a ‘vicious cycle’ of two-way dependence which is hard to break for potential sources with less official status, such as representatives of various Indigenous communities.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".