Corporate interests within transnational advocacy networks: The International Coalition Against Plain Packaging
Bibliographic record
Abstract
Abstract Most of the research on transnational advocacy networks documents progressive, voluntary movements, motivated by values associate with human rights and public goods. There is little critical reflection on the role of corporations within such networks or on the material motivations behind movements. Meanwhile literature on corporate political strategies related to partnerships with civil society is limited to national level analysis. This article presents a case study of the International Coalition Against Plain Packaging, which is conceptualized as a transnational advocacy network, and documents its links to the tobacco industry. We find that, not only have tobacco companies provided network members – publicly presented and perceived as independent – with financial resources, but they have also been involved in producing the information used by the network to debate the benefits of plain packaging. In return, the tobacco industry is able to propagate ideas favorable to its interests through organizations perceived as legitimate experts, and to maintain a network of allies ready to counter tobacco control regulations when and where they arise. Considering the multiple benefits corporations might derive from engaging with transnational advocacy networks, there is need for greater research on private actors’ influence within advocacy networks and on those networks that aim to counter or advance alternatives to progressive ideals.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".