Bibliographic record
Abstract
Scientific integrity was a prominent issue in the Canadian federal election of 2015, with incumbent Conservative Party prime minister Stephen Harper being increasingly criticised for appearing to dismiss evidence that did not support his agenda. In the lead-up to the election, Liberal Party leader Justin Trudeau focused on this issue, committing to a number of actions to strengthen evidence-based decision making in Canada. Many believe this played a role in his successful election as prime minister. Of course, Canada’s federal government is not the only, or even the ultimate, authority on evidence production and use within Canada. Provincial, territorial and municipal governments, as well as individual public sector organisations, also hold various policy- and decision-making powers. However, it is the case that the value placed on evidence by the highest level of government – and the ways in which that value is enacted, for example, through funding – is without doubt influential, and it provides an important context for this chapter. Although evidence – the available body of information indicating whether a proposition is valid – is produced and used in many sectors, in this chapter we draw on our experiences as a clinician/researcher (Straus) and a researcher/funder (Holmes) to focus primarily on Canada’s healthcare system. Under this system, which serves a culturally diverse population of 36 million people across a largely rural and remote landscape (apart from a few major cities), all Canadian residents have access to medically necessary services without charge. Federally, the government sets national standards for the healthcare system and supports healthcare for specific groups (for example, indigenous populations, serving members of the Canadian Forces, inmates of federal penitentiaries and some refugee claimants).
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 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".