Bibliographic record
Abstract
Purpose From a perspective inside one of the most advanced of the state programs, this presentation aims to explore issues of whom are we trying to reach; what information are we trying to convey; when did this reporting start; where can anyone find reports; why are we doing this; and how does it work. This is, however, neither a typical consumer informatics problem nor a subject that public health is used to dealing with. Design/ methodology/ approach The paper is a narrative review of personal experience. Findings Despite achievements, there are fundamental knowledge gaps and unsubstantiated assumptions underlying mandatory public reporting. Research and better role delineation are urgently needed to optimize current choices and ultimately determine whether this is the most cost‐effective strategy among alternative prevention investments. Practical implications Public health departments are in uncharted territory with this new area of activity, faced with fundamental knowledge gaps that potentially hamper chances of success. Perspectives explored in this part of the Universities Council Symposium help frame a research agenda and guide evolution of less advanced programs. Originality/value The Universities Council, established and coordinated by Washington State's HAI Program, is unique in taking an interdisciplinary approach to comprehensive examination of the unsubstantiated assumptions underlying mandatory public reporting.
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 | 0.091 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".