Bibliographic record
Abstract
How to Set the World on Fire T he year 2010 was a busy one, but I didn't expect it to go out with a bang -literally.I was visiting friends in Copenhagen to celebrate the new year.We were getting ready to start the countdown to midnight when the building shook.Puzzled, we looked outside to find that someone on the street below had accidentally shot a firework into the apartment building.Luckily, its builders made sure, decades ago, that its structure was sturdy, and the rocket hit a wall, not a window.It took only a moment before the pyrotechnic display resumed, this time striking a building on the other side of the street.I couldn't help thinking that setting off elaborate fireworks on a residential street, likely after having drunk too much to walk a straight line, would get you arrested in many parts of the world, whereas in Copenhagen on New Year' s Eve, it was no cause for alarm.On the other hand, crossing one of the city' s streets against a red light, even if there are no cars in sight, is likely to get you a lecture from a passer-by.Health practices and policy also provide many examples of diverging and changing expectations for what falls within accepted norms.Think about smoking in public places, care for elders or how best to treat any number of health problems.In my mind, many of the interesting questions are about change, and what either impedes or promotes it.for example, how do clinical innovations progress to evidence-based practice and, from there, to incorporation into practice guidelines and acceptance as standards of care?How does an idea become "the way we do things here" or fall out of favour?Why do some ideas take off, while others stall early on?In this issue of Healthcare Policy/Politiques de Santé, our authors tackle these types of questions and try to inform future practice and policy development.for example, while Stephen Duckett is no longer with Alberta Health Services, his paper on Alberta' s approach to healthcare reform provides a window into the thinking behind recent developments in that province.In another paper, michael Law and colleagues aim to inform a debate that has produced at least as many fireworks -the likely effects of changes in the pricing of generic drugs in Ontario.Likewise, Roger Chafe and colleagues look into another aspect of drug policy as they explore variations in expenditure on cancer drugs across the country.I could go on, but citing Roger' s paper allows me to mention that this issue of the journal marks his transition into Healthcare Policy's editorial group, taking over from Christel Woodward, whose term as an editor has come to an end.Roger is director of paediatric research in the faculty of medicine at memorial university, and he brings a breadth of exper-
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.094 | 0.068 |
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".