Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it