Critical health education studies: Reflections on a new conference and this themed symposium
Bibliographic record
Abstract
In May 2018, a group of scholars gathered in the icy and sunlit grandeur of Queenstown (Aotearoa New Zealand) to talk, debate and share ideas about health education. The conference aimed to trouble and disrupt traditional kinds of health education and, instead, suggest possibilities for the critical study of health education – both in terms of theory and practice. This introduction to the special themed symposium is a reflection by the six authors on that new conference – Critical Studies in Health Education (CHESS) – and what it aimed to achieve. The authors discuss and define the intent of critical approaches to health education, and reflect on their experiences of the conference, as well as the future of the field. Papers in this special themed symposium of Health Education Journal are also introduced.
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 | 0.107 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.038 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.009 | 0.038 |
| Research integrity | 0.033 | 0.084 |
| Insufficient payload (model declined to judge) | 0.009 | 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".