Non-profit advocacy and discursive opportunity structures: the case of Ontario’s sex education debates
Bibliographic record
Abstract
This thesis critically explores the role of non-profit organizations in advancing sexual and reproductive health and rights (SRHR) in Canada through education policy advocacy. In 2018 and 2019, there were heated debates in Ontario regarding changes to the provincial Health and Physical Education Curriculum, also known as a sexual health education curriculum. Using the concept of discursive opportunity structure, this qualitative case study examines the framing strategies used by non-profit organizations to advocate for curriculum reforms. It situates advocates’ discursive opportunities within the broader ideational context of the sexual and reproductive health and rights movement and asks how environmental constraints within the non-profit sector shape frame selection to align with or diverge from the movements’ various aims and objectives.Semi-structured interviews with non-profit representatives are used to support discussion of how the non-profit environment influences the ways in which organizations can and do advocate for policy change. Findings in this case show strong alignment of non-profit collective action frames to their discursive opportunities, demonstrating a highly strategic approach to policy advocacy within the sector. There were limited discursive opportunities for claims-making regarding the structural roots of inequities in access to SRHR in Canada, and fear of government retaliation for dissent through the cuts to organizational funding is a strong factor shaping organizations’ perceptions of their discursive opportunities
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.075 | 0.053 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".