Bibliographic record
Abstract
Abstract Canada is a wealthy country with an enviable healthcare infrastructure and publicly funded healthcare system. As a result, it might seem difficult to believe that reproductive healthcare is not always readily accessible in Canada. Yet despite living in a high-income country with favorable reproductive health laws, Canadians seeking reproductive health services often face significant barriers to access. Canadian policymaking around reproductive health has proceeded by way of an ad hoc approach, addressing concerns as they arise, rather than attempting to craft a coherent policy strategy. The vast array of conditions and concerns included in reproductive health poses a challenge for policymaking, as do other factors, including the politicization of reproductive decision-making and Canada’s constitutional division of powers. In the absence of a comprehensive reproductive health policy to consider, this chapter discusses law and policy through the use of a few illustrative reproductive health services—contraception, abortion, and assisted reproduction—to illustrate the operation of reproductive health policy in Canada’s federal system. The focus will be on access to reproductive health services and the role that policy attention to reproductive health (or the absence of such attention) plays in facilitating or frustrating access.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".