POGG as a Basis for Federal Jurisdiction over Public Health Surveillance
Bibliographic record
Abstract
In the aftermath of Severe Acute Respirato- ry Syndrome (SARS) and with concern growing about avian flu, mad cow, and other emerging diseases, public health surveillance has become a matter of importance to Canadians. Such sur- veillance is a key component of the fight against these diseases; it involves the systematic collec- tion, analysis, interpretation, and dissemination of data about health-related events for use in public health responses. Indeed, new technolo- gies enable “data mining” at an unprecedented scale, both in the amount and type of informa- tion that can be collected, and in the extent to which that information can be used to identify public health concerns. All this has made the concept of “anonymous” information less and less realistic.
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 | 0.079 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".