Bibliographic record
Abstract
To the Editor; I was delighted to read in the 2009 October issue of Paediatrics & Child Health not one but two reports addressing injuries. The Public Policy Advocacy contribution from Safe Kids Canada was especially welcome and timely. I urge all readers to follow Mr Cuzzolino's request to show their support for the new legislation by writing to Senators and the Minister of Health. As one newspaper report noted, some Liberal senators have objected to the bill because it gives inspectors too much power! (1). I have been following this latest setback with growing concern: this long overdue reform is essential if we are to properly protect our children. Journal readers may be interested to learn that my most recent attempt to support this reform fell afoul of what amounts to censorship by Health Canada. When a recent publication entitled: “Child and Youth Injury in Review, 2009 Edition – Spotlight on Consumer Product Safety” (2) was being finalized, I was asked to write the introduction. I assume that the invitation arose because of my long relationship with Canadian Hospitals Injury Reporting and Prevention Program – the data that were most prominently featured and my longstanding interest in product safety (3). However, my draft identified many serious shortcomings in the document and I was urged to revise. I clenched my teeth and wrote something far more generous that was judged acceptable. Then, after someone read the final paragraphs more carefully, I was told that they could not possibly publish what I had written because it was ‘critical of the government’.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.038 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".