Challenging the Dominant Discourse: Framing and the Social Determinants of Health Equity on Trade Policy Agendas – The Case of the Trans Pacific Partnership Agreement
Bibliographic record
Abstract
Trade agreements influence the distribution of money, goods, services and daily living conditions – the social determinants of health and health equity, which ultimately impacts differentially on health within and between countries. In order to advance health equity as a trade policy goal, greater understanding is needed of how different actors frame their interests in order to shape government priorities, thus helping to identify competing agendas across policy communities. This paper reports on a study of how policy actors framed their interests for the Trans Pacific Partnership agreement. We analysed 88 submissions made by industry actors, not for profit organisations, unions, researchers and individual citizens to the Australian government during treaty negotiations. We show that policy actors’ ideas of the purpose of trade agreements are shaped by competing underlying assumptions of the role of the state, market and society. We identify three primary framings: a dominant neoliberal market frame, and counter frames for the public interest and state sovereignty. Our analysis highlights the potential enabling and constraining impact of policy frames for health equity. In particular, the current dominant market framing largely excludes the social determinants of health and health equity. We argue that advocacy needs to tackle head on the underlying assumptions of market framings in order to open up space for the social. We identify successful examples of health framing for equity as well as opportunities for engagement with ‘non-traditional’ allies on shared issues of concern.
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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 | 0.074 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.023 | 0.068 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| 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".