Health Misinformation Research
Bibliographic record
Abstract
ABSTRACT Health misinformation research has dramatically increased since the start of the COVID‐19 pandemic, although not all of this work has taken advantage of the rich theoretical and methodological background information science as a discipline has to contribute. This panel presentation will feature information scientists conducting health misinformation research from and in various settings, to showcase the value and range of information science approaches to health misinformation research. Each panelist will describe their work in the area of understanding and/or addressing health misinformation in institutions such as libraries and schools, health systems and interventions such as vaccination or public health promotion, or the general public in their online or offline information environments. Presentations will also highlight the ways researchers are applying information science theory, methods, and/or approaches to health misinformation topics, as well as lessons panelists have learned through this work. The panel will conclude with an interactive audience discussion that will center on the ways in which information science can help understand and address health misinformation challenges around the world.
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 | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".