Bibliographic record
Abstract
While management gurus point out that the 'you can't manage what you can't measure' dictum is not universally true, robust measurement of progress -or the lack thereof -can provide clarity, transparency and drive.In this spirit, federal, provincial and territorial governments recently endorsed a set of measures to support the 10-year investment agreement made in 2017 (Government of Canada 2018).The 12 agreed measures were evenly split between indicators of access to mental health and addictions services and indicators of access to home and community care (Box 1).The Canadian Institute for Health Information (CIHI) is slated to begin annual reporting in 2019.(Note: The federal government and Quebec agreed to an asymmetrical arrangement.Likewise, given the then-recent election in Ontario, the province could not officially endorse the recommended measures.)
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 | 0.025 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".