Above and Beyond Broadcasting: a study of First Nations media and the Covid19 pandemic
Bibliographic record
Abstract
The expanding Covid-19 pandemic continues to threaten the safety of communities across Australia, including First Nations communities in rural, remote and suburban areas. From the very early stages of the pandemic response, First Nations media outlets have risen to the challenge of supporting and communicating with Indigenous people and broader audiences by providing targeted, relevant and reliable information and by fostering connections with individuals and between groups. The First Nations media sector proved to be a trusted source of information by tailoring messages to suit its audiences and by correcting emerging misinformation. It recognised and continues to address mental health issues associated with the pandemic by maintaining its focus on the welfare of audiences. It has overcome obstacles and innovated with new forms of programming specifically designed to cater for community needs. First Nations media organisations also demonstrated the important role they play in fostering identity and keeping communities strong and by often going above and beyond broadcasting and communicating through media channels. The sector sees itself as a central influence in the lives of communities, which means it has tried to be present for audience members, not only on air and through media channels, but physically on the street or over the phone or at community events. Its role was, and remains, to help communities survive the pandemic and emerge strong and resilient. However, the evolving crisis has also highlighted that First Nations media has ongoing challenges with resources, staffing and training and in order to continue to meet the needs of the diverse audiences it serves, these challenges need to be addressed. First Nations media organisations have adapted their crisis response to the pandemic to focus on vaccination information and managing information flow about the evolving directives for travel and lockdowns on an ongoing basis. Through case study examples, this study has generated understanding about how First Nations media organisations operated during the early days of Australia’s COVID-19 pandemic. It has identified key lessons that can be learned from that experience, both for the future benefit of media organisations and for those First Nations communities continuing to struggle with the impact of Australia’s most urgent public health challenge in nearly a century.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".