Bibliographic record
Abstract
An organization can drive quality only through its people. Too often, we relegate quality to a single department or a small group of evangelical leaders but fail to make it everyone's business. Accountability has become a buzzword, and we have translated it into huge agreements with myriads of measures and indicators, all purporting to have something to do with quality.Institutions need to focus on a few things to improve quality. How do you build a culture? You plan, you pick certain goals to which you aspire, you set targets and you measure against those targets. You provide the skills, knowledge, expertise and infrastructure necessary to enable people to meet those targets, and then you drive for them. And you are transparent about it.I often think that we overcomplicate quality. As I have said repeatedly, it is as simple as choosing a measure, planning to implement some changes and re-measuring to see if your changes have had any impact. You don't need a national council, or even a provincial one, to make quality happen in the day-to-day operations of every healthcare organization in the country. Rather, you just need to get started.
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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.043 | 0.058 |
| Insufficient payload (model declined to judge) | 0.035 | 0.024 |
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".