Health Information Science: Perspectives on a Discipline in Development
Bibliographic record
Abstract
This panel convenes six emerging scholars in the area of health information science, to trace some of the multiple pathways taken by this pluralistic discipline in research, practice and policy areas. How is HIS developing as an academic discipline? In describing the conceptual and methodological concerns of their work, presenters will raise some of the live questions shaping a health information science lens, including: - What practice and policy sectors contain pressing HIS questions right now?- What methodologies are most saliently informing research production in HIS?- What theoretical approaches have been tried in current and ongoing HIS research?- How is knowledge translation effected in HIS? Ce panel réunit six chercheurs émergents dans le domaine de la science de l'information sur la santé (SIS), afin de retracer certaines des multiples voies empruntées par cette discipline pluraliste dans les domaines de la recherche, de la pratique et des politiques. Comment la SIS évolue-t-il en tant que discipline universitaire? En décrivant le concept et la méthodologie préoccupations de leur travail, les présentateurs soulèveront certaines des questions en direct qui façonnent le prisme des sciences de l'information sur la santé, notamment:- Quels secteurs pratiques et politiques comportent actuellement des questions urgentes relevant de la SIS?- Quelles méthodologies sont les plus importantes dans la recherche dans la SIS?- Quelles approches théoriques ont été utilisées dans les recherches actuelles et en cours sur la SIS?- Comment les connaissances sont-elles appliquées dans la SIS?
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 | 0.101 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.018 | 0.105 |
| Scholarly communication | 0.050 | 0.054 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.023 | 0.025 |
| 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".