The piety of optimization: The rhetoric of health awareness in ParticipACTION and Fitbit
Bibliographic record
Abstract
This article uses the tools of rhetorical study to investigate how health awareness, as both a concept and a set of beliefs that reinforce ideals of health, permeates everyday life and affects ways of being. I explore how health awareness is communicated through both public health and commercial marketing campaigns, and argue that as the sources of information change, so too do the ideas of health that we are asked to be aware of. Through an analysis of the websites of ParticipACTION, a publicly funded health and fitness campaign, and Fitbit, a corporation that produces wearable technologies, I show that these organizations provide their audiences with instructions for self-conduct in the pursuit of health through the piety that time is a resource to be managed. Through this piety, ParticipACTION and Fitbit's websites each reify an altar of health where health is represented as a socially and physically fitter (optimized) self, always just out of reach and attainable in the future. I conclude with a call for critical descriptions of health awareness to move beyond the explanatory power of neoliberalization of health, and turn to the work of Rachel Sanders, Annmarie Mol, and Donna Haraway as possible avenues for resisting optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".