Transnational corporations and oral health inequalities; an introduction.
Bibliographic record
Abstract
In an increasingly globalised world, Trans-National Corporations (TNCs) wield considerable social, economic and political influence, both in the international market economy and within individual countries. The practices of TNCs can have positive or adverse effects on population health through production methods and products, shaping social determinants of health, or influencing the regulatory structures governing their activities. TNCs can contribute to health inequities if the health consequences arising from their practices have disproportionate adverse impacts on vulnerable populations or positive benefits for less vulnerable groups. Despite growing recognition of the implications for health, including oral health, arising from TNC practices, little research has sought to systematically assess the oral health and/or oral health equity impacts of TNCs. In the four papers that follow, we contribute to the discourse around oral health-related inequalities through the lens of power, human agency and TNCs. The papers formed the basis of a symposium entitled 'Transnational Corporations and oral health inequalities' at the 97th General Session of the International Association of Dental Research held June 2019 in Vancouver, British Columbia. The authors responded to the 2014 Lancet-University of Oslo Commission on Global Governance for Health call for greater attention to the health effects of TNC practices and the regulatory regimes in which they operate. The papers overview the role of TNCs in oral health inequalities at an international level, with a specific focus on illuminating their far-reaching influence on our everyday lives, from both epidemiological and sociological perspectives, and the multi-faceted positive or adverse effects on oral health this might have. Key TNC examples are provided by way of the sugar and the tobacco industries, with their impact on dental caries, periodontal diseases and head and neck cancers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".