Bibliographic record
Abstract
ou know when Janet Davidson is in the room.A nationally renowned health executive with more than 30 years of experience in government, voluntary, hospital and community sectors in Alberta, Ontario, Saskatchewan, Manitoba and British Columbia, she recently did two remarkable things: left the C-suite to become a global consultant in healthcare, and returned to Alberta when that province called earlier this year.With a long list of credentials and experiences, and the gratitude of a country as officer of the Order of Canada in 2006, Davidson and her resume are, quite possibly, peerless.When she assumed her role as official administrator of Alberta Health Services (AHS), Davidson was the Canadian Executive of KPMG's Global Healthcare Centre of Excellence.Prior to that, she was president and chief executive of Trillium Health Centre in Mississauga where, with a merger with the Credit Valley Hospital, she helped create the largest community academic hospital in Canada.She is presently a member of board of directors for the Canadian Institute for Health Information (CIHI) and serves as the chair of the CIHI Board's Governance Committee.Until recently, Davidson was a member of the board of the Ontario Institute for Cancer Research, and she is the immediate past chair of the Ontario Hospital Association.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 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".