The slaying of facts by dubious hypotheses and ugly misrepresentations: the musings of a Canadian editor on the 2009 health-care debate in the United States.
Bibliographic record
Abstract
It has been a cool summer in eastern Canada, but as we move into August it is warming up. This change in temperature can be attributed not just to climatic conditions but also to the heat that is being generated by the health-care debate south of the border. We Canadians find ourselves in the uncomfortable position of being pawns in this debate — bandied about by the different interest groups in the United States. Canada is portrayed as having either the world’s best health-care system — warts and all — or the worst. Canadians are either the healthiest and luckiest people on earth or the poorest of saps just a heartbeat away from death because of an inadequate health-care system. The groups who condemn the Canadian system are the most vitriolic and are the ones most responsible for the hot air blowing our way. When these opponents of health-care reform use the word “Canadian,” it is with disdain, pity, and fear. It is as if we were a country infected with a deadly virus — ready to bring ruin to the United States. [...]
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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 | 0.054 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.052 | 0.061 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".