Bibliographic record
Abstract
From the editor-in-Chief a time for renewal and redirectionIn response to the unseasonable warmth of an early spring, new growth is blossoming all around us.Unlike any spring in recent times, a new energy also envelops us as we emerge from the hype and fervour of a very successful Olympics.Across the country, Canadians found themselves replete with national pride and a seemingly new-found patriotism.I loved the entire experience, but it left me wishing that there was a comparable event for nursing, an opportunity to showcase our excellence to the country and to experience that nationalistic sense of our unity as a profession.Although our profession has many celebratory events recognizing nurses and their contributions to the health of Canadians countrywide, I wonder how many, and how often, other Canadians take note and celebrate the centrality of nurses to health services delivery in this country?No question that displays of excellence happen every day in every care setting.We know that without nurses, the health system would be bereft of many of the qualities we value as citizens and as care providers.But I wonder -would there be a public outcry if nurses ceased to be the primary caregivers in a majority of care settings, replaced by less educated, less knowledgeable, less skilled, non-professional workers?
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.168 | 0.153 |
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".