La quantification des habitudes et du corps: Le mHealth comme technologie politique du corps
Bibliographic record
Abstract
Resume : Les technologies de quantification des habitudes et du corps sont percues comme des solutions efficaces et peu couteuses pour regler ce qui est qualifie de probleme d’obesite de la population. En abordant les technologies selon une ontologie de la Technique, il nous semble cependant important de remettre en question les imperatifs dont elles seraient porteuses. Issues des theories de la cybernetique, les mHealth pourraient ne pas permettre l’ empowerment des individus qui est escompte. De plus, la responsabilisation comme technique de prise de conscience de l’individu se revelerait etre une norme promue par le nouveau paradigme de sante. Nous argumentons que non seulement les technologies mobiles de sante s’inscrivent dans celui-ci, mais, egalement, representeraient et contribueraient a promouvoir des imperatifs propres a la Technique et a l’ideologie neoliberale. L’article conclut en proposant de considerer les mHealth comme des technologies politiques du corps participant a la creation de savoirs et, donc, de pouvoir sur les corps. Abstract : Technologies that quantify habits and body (commonly designated as mHealth) are seen as efficient and costless solutions to the socalled obesity problem that are facing populations around the world. Through the vision of the Technique’s ontology, it seems important to question its proper imperatives. From the point of view of cybernetic’s theories, we could consider mHealth as not as empowering as it is expected for individuals. Furthermore, responsabilisation as a selfconscious and selfmanagement technique could be seen as a norm from the new health paradigm. We argue that mobile health technologies are part of it, but, moreover, represent and contribue to promote the Technique’s and neoliberalism’s imperatives. We conclude in proposing to consider mHealth as political body technologies acting in the creation of knowledge and, so, of power on the bodies.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".