Canada’s emerging public health infostructure
Bibliographic record
Abstract
the public health context.Broadly, this includes tools to help locate and share information, enabling access to information, the provision of repositories for information and the promotion of practices to facilitate knowledge translation for successful use by frontline public health staff.5. Skills enhancement, both to support the many advances in information technology as well as to maintain and upgrade the core skills of public health professionals.A strategy that can be widely adopted yet is cost effective is being pursued including, for example, the use of distance learning tools.6. Enterprise architecture, by which we mean the maps and reports that describe the elements of the public health business from the program and technology perspectives along with the linkages between the components.This work is based on the Zachman framework for enterprise architecture.Currently it addresses programs in communicable disease, immunization and vaccine associated adverse events and includes not only the current state of programmes but also a vision for a new architecture.7. Funding and governance that includes the co-operative relationships and accountabilities of an undertaking with many distinct partners in order to move forward in a pragmatic yet effective way.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".