Bibliographic record
Abstract
The symposium was held at Ewha Womans University in conjunction with the 7th Annual Qualitative Health Research Conference and was supported by the Korean Academic Society of Women's Health. Approximately 100 participants attended from six countries: Australia, Canada, China, Japan, Korea, and the USA. The abstracts of this symposium consist of two parts: (A) Invited Presentations, and (B) Concurrent Sessions. Among the invited specialists Dr Young Hee Choi, member of the Korean National Assembly, delivered an impressive speech on ‘Health‐care policy for Korean elderly’. The speech helped all to better understand the health care of the elderly at present, as well as future plans for care in Korea. The presentation by Dr Beum Seang Kim (Catholic University, Korea), ‘What is Alzheimer's disease?’, gave the attendees an opportunity to gain greater knowledge about dementia and Alzheimer's disease. The other invited speakers, Drs Wendy Duggleby, Kiyoko Makimoto, Chun‐Ok Lee, and Kaoru Nishimura also provided impressive and stimulating addresses. In addition to invited presentations, 20 papers were presented at the concurrent sessions.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.400 | 0.183 |
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".