Bibliographic record
Abstract
Most Canadians have long-standing familiarity with images of Jean Francois Gravelet crossing the Niagara River (Niagara, Ontario) on a tightrope on June 30, 1855. Pictures of the famous incident show the ‘Great Blondin’, as he was known, balanced precariously over the Niagara River Gorge. In fact, the Great Blondin made many crossings of ever increasing difficulty, either by walking, running or cycling. On one occasion, he pushed a wheelbarrow to the centre of the rope where he stopped, cooked and ate an omelette made on a small stove. The culmination of his efforts came with carrying his manager across in a special shoulder harness. We have chosen Blondin's feats as a metaphor for the hazards of drug prescribing for children. How good is this metaphor? Even funambulists, or tightrope walkers, do not venture forth without some substantial aids. For starters, they have the fundamental support of a stout rope or wire, and a balancing bar that serves to distribute the risk. Often they enjoy the additional security afforded by a safety net.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".