Bibliographic record
Abstract
Noni MacDonald took on the daunting task of inaugural editor of Paediatrics & Child Health (fondly referred to as ‘PCH’ by those of us ‘in the know’) 20 years ago. This was a high-risk endeavour because a previous Canadian Paediatric Society (CPS) journal lived a very short life until the publisher went bankrupt. Noni's goal was never that PCH become a competitor for the mainstream paediatric journals. Instead, Noni was speaking the language of ‘knowledge translation’ (presenting research findings in such a way that clinicians start using them in their clinical practice) before that term was even coined. She constantly reminds everyone that readers want short and clear articles that have a ‘take-home’ message. In fact, she may be less than pleased that we are using up a precious page in PCH for this tribute to her. On Noni's watch, PCH achieved the major milestone of being indexed in PubMed and achieving a respectable impact factor for a Canadian specialty society journal. This step markedly increased the number of readers who have access to the Journal. Before this, Noni spearheaded a program in which paper copies of PCH were mailed to interested physicians with limited Internet access in resource-poor countries to try to improve the state of global paediatric care.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.032 |
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".