Governing through (in)security: a critical analysis of a fear-based public health campaign
Bibliographic record
Abstract
Fear appeals are once again popular in public health campaigns aimed at preventing unhealthy behaviors and dangerous life practices such as smoking, unsafe sexual practices, drug use, alcohol abuse, impaired driving, etc. Every year in the province of Quebec (Canada), a new prevention campaign for sexually transmitted infections is launched by the SLITSS (Service de lutte contre les infections transmissibles sexuellement et par le sang). In 2006–2007, the SLITSS created a fear-based campaign entitled ‘Condoms: They aren’t a luxury’ for the prevention of sexually transmitted infections in young adults. The purpose of this paper is to share the results of a discursive analysis of the documents retrieved from this campaign, which was developed using commercial advertising and marketing strategies. Using situational analysis and the mapping process proposed by Clarke (2005 Clarke, A. 2005. Situational analysis: grounded theory after the postmodern turn, Thousand Oaks, CA: Sage. [Crossref] , [Google Scholar]), we critically examine the use of fear appeal in the campaign. Drawing on Michel Foucault's concepts of governmentality and bio-power, we assert that fear should be understood as a bio-political technology deployed to manage/govern young adults’ sexual practices. In doing so, we critique the use of fear as a strategy to create a state of permanent (in)security and challenge the adoption of commercial advertising and marketing strategies to develop public health campaigns.
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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.026 | 0.055 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".