Bibliographic record
Abstract
Although health surveillance in schools is not a new phenomenon, surveillance has arguably intensified in the contemporary historical moment. As individuals and professional collectivities coalesce around the concern for youth health, surveillant mechanisms proliferate within the educational context. In this article, I critically examine the “Youth Health Survey” (YHS) administered in Manitoba (Canada), to illustrate how youth health is deployed as a mechanism for engaging inter- and intraprofessional knowledge in tightening the biopedagogical discourse surrounding youth health. In Manitoba, these biopedagogical networks and lessons arise from the body data gathered through the surveillant mechanism of the YHS resulting in the formation of public partnerships, resource sharing, and collaborative approaches to intervene in youth lives, both inside and outside the school, forming a “surveillant assemblage” of youth health. Using qualitative data from interview and focus groups with health professionals and education specialists, I illustrate how an ever-tightening web of professional networks invested in shaping the future of youth lives connect through the development and deployment of a health surveillance tool.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.053 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".