The role of metaphor in the corporate political strategies of health harming industries: Comparing the concept of balance in the gambling and opioid industry discourses
Bibliographic record
Abstract
Scholars have identified notable similarities between the political strategies employed by health-harming industries. This includes similarities in the narratives employed by industry actors seeking to oppose public health regulations that threaten their commercial interests. This study seeks to examine the use of a specific concept - the balance metaphor - in the policy discourses of two health-harming industries. Namely, the pharmaceutical industry implicated in the prescription opioid crisis in the US, and the UK gambling industry, whose products and practices are associated with a serious, but largely neglected, series of harms. We first review research on metaphors, demonstrating how this provides additional theoretically-informed concepts with which to understand how industry discourse circumscribes the terrain of policy debates in ways amenable to commercial interests. Building from these insights, we conducted a rhetorical analysis, examining how the concept of balance is employed by different actors in distinct contexts to shape understandings of the social and policy problems associated with gambling and opioid products and to promote industry-favourable regulatory responses to these. This brings a micro-level of analysis to supplement previous meso- and macro-level scholarship in this space. We use our findings to argue that the depoliticization of the policy process and objectivization of the policy space - in ways that obscure its contingent and political nature - through discourses of balance is itself an arch political act. Examining the metaphors used in policy debates and their functions provides important insights that can be used to inform the construction of counter-narratives to industry-favourable discourses, including the creative use of novel metaphors in the service of public health goals.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| 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".