The influence of corporate market power on health: exploring the structure-conduct-performance model from a public health perspective
Bibliographic record
Abstract
BACKGROUND: The detrimental impact of dominant corporations active in health-harming commodity industries is well recognised. However, to date, existing analyses of the ways in which corporations influence health have paid limited attention to corporate market power. Accordingly, the public health implications of concentrated market structures, the use of anti-competitive market strategies, and the ways in which market power mediates the allocation and distribution of resources via market systems, remain relatively unexplored. To address this gap, this paper aimed to identify and explore key literature that could inform a comprehensive framework to examine corporate market power from a public health perspective. The ultra-processed food (UPF) industry was used to provide illustrative examples. METHODS: A scoping review of a diverse range of literature, including Industrial Organization, welfare economics, global political economy and antitrust policy, was conducted to identify important concepts and metrics that could be drawn upon within the field of public health to understand and explore market power. The Structure-Conduct-Performance (SCP) model, a guiding principle of antitrust policy and the regulation of market power, was used as an organising framework. RESULTS: We described each of the components of the traditional SCP model and how they have historically been used to assess market power through examining the interrelations between the structure of industries and markets, the conduct of dominant firms, and the overall ability of markets and firms to efficiently allocate and distribute the scarce resources. CONCLUSION: We argue that the SCP model is well-placed to broaden public health research into the ways in which corporations influence health. In addition, the development of a comprehensive framework based on the key findings of this paper could help the public health community to better engage with a set of policy and regulatory tools that have the potential to curb the concentration of corporate power for the betterment of population health.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".