What Do We Mean by ‘Representation’? Towards a Systematic Corpus-Assisted Critical Discourse Analysis of First Nations People(s) in Australian Print News
Bibliographic record
Abstract
Given the enduring influence of news media on public awareness and opinion of Aboriginal and Torres Strait Islander people(s) and related issues, systematic analysis of how these communities are represented in news media remains socially significant. However, what analysts mean when they use the term ‘representation’ shows considerable variation across the literature, from the amount of coverage relevant stories are afforded, to the inclusion of First Nations sources, to the narratives that a given version of events constructs. Moreover, while language is central to the discursive construction of these matters, few linguistic studies of Aboriginal and Torres Strait Islander media representations exist. To begin to address these gaps, this study first maps the forms of representation identified in previous research, before analysing three of the four types (visibility, naming strategies and portrayal) via a corpus-assisted critical discourse analysis of news articles about Aboriginal and Torres Strait Islander people(s) and issues. It finds that, by and large, how First Nations people(s) are represented is inconsistent—while in some areas, representation broadly aligns with expectations expressed by First Nations communities (i.e. for increased coverage, respectful terms of reference and strengths-based discourses), problematic practices persist. Importantly, however, the analysis also illuminates a range of journalistic practices that can be implemented to improve representation in areas currently lacking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".