Governance Through Mobile Apps: \nThe Construction of Public Health Problems
Bibliographic record
Abstract
ABSTRACT \nGovernance Through Mobile Apps: \nThe Construction of Public Health Problems \n \nCarmen Lamothe \n \nMobile phone applications (apps) for health are proliferating at a tremendous rate and public health agencies are now starting to offer their own apps as a tool for promoting public health information. Taking a critical public health perspective, this thesis examines the emerging use of mobile apps by public health agencies and the ways that public health apps form and frame public health problems. It first provides a descriptive account of apps promoted by the Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC). Then, using a Foucauldian inspired policy analysis methodology (Bacchi 2009) two apps from the CDC are examined. These apps constructed public health problems in ways that explicitly held some groups accountable, while implicitly absolving others. The findings in this thesis illustrate that the app design process raises important (and under-researched) questions about how apps may come to emphasize certain types of public health knowledges and politics. The discourses on these apps can be understood as a guide to healthy behaviour, instructing subjects how to become productive and responsible. Findings highlight how apps can be understood as "mini-policies" where public health problems and policies come to be refracted and reframed.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".