Disrupting the social discourse of First Nations' food insecurity: What can dietitians do?
Bibliographic record
Abstract
Colonisation and racism amplify the complexity of food insecurity for First Nations peoples. Attitudes towards food insecurity have deep roots in this country’s colonial history and social policies and embed themselves in the Australian narrative. Media representation of food insecurity has the power to shape social values and policy responses, and our views as dietitians. This study explored how food insecurity for First Nations peoples is represented in the Australian media and by health staff. Using social constructionism and critical discourse analysis (CDA), we studied the historical, political and social contexts that influence perceptions of and responses to food insecurity. Using CDA as a method, discourses were interpreted that reflected power operating through language. This study analysed data from 75 media articles about First Nations food insecurity and interviews with 14 staff in Aboriginal and Torres Strait Islander primary health services in southern Meanjin. The media analysis discourses reflected personal responsibility, moral failure and the safety of children. Health staff representations centred on ‘strengths’, ‘struggle’ and ‘shame’ discourses. Although grounded in relationships and helping, staff focused on individual and charitable responses layered with judgement of deservedness. Associations between food and child safety were seen across media and staff discourses. Settlers have weaponised food since colonisation. ‘Not being able to put food on the table’ continues to be implicated with blame, risk and shame for First Nations peoples. As dietitians, we must challenge our perceptions about food insecurity’s causes and solutions and consider its loaded history when we talk about food.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".