Exporting pandemics: free trade agreements and the global diffusion of unhealthy behaviours
Bibliographic record
Abstract
This thesis is about the causes of unhealthy behaviours, and the role of Free Trade Agreements (FTAs) in shaping them. Unhealthy behaviours include smoking, harmful alcohol consumption, and excess caloric intake. Sociologists have primarily investigated the causes of these behaviours with reference to individuals’ socio-economic and local environmental characteristics. Yet these determinants may, in turn, be shaped by a society’s macro-economic and political institutions, including FTAs. FTAs are major policy instruments that are increasingly being used to promote cross-border trade and investment. In doing so, FTAs may unintentionally foster the cross-border diffusion of unhealthy behaviours and constrain governments’ abilities to regulate them. However, scholars’ understanding of whether and how these impacts prevail is limited: most prior analyses precluded causal conclusions, whilst more rigorous statistical analyses of FTAs primarily focussed on economic outcomes. In this thesis I address these gaps. In the first empirical chapter I used a natural experiment design to evaluate whether entering into an FTA with the US corresponded to a rise in caloric intake in Canada. In a second analysis I used the synthetic control method to disentangle the specific clauses within FTAs that lead to dietary changes. In a third study I created a new dataset to investigate how FTAs might constrain governments’ abilities to introduce regulations aimed at preventing unhealthy behaviours. These empirical chapters advance previous analyses of FTAs and unhealthy behaviours by providing more robust evidence to infer a causal effect of FTAs and by elucidating their pathways to impact. They also make two broader, inter-related contributions to social scientific scholarship. First, my findings demonstrate the importance of FTAs for sociologists’ understanding of the causes of unhealthy behaviours. Second, my thesis shows that FTAs can have detrimental consequences for a society’s well-being in ways that are often overlooked in economic FTA evaluations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".