How can civil society organizations contribute to the scale-up of comprehensive sexuality education? Presentation of a scaling framework illustrated with examples from Indonesia
Bibliographic record
Abstract
Comprehensive sexuality education (CSE) can substantially contribute to the health and well-being of young people. Yet, most CSE interventions remain limited to the small piloting or research phase and scale-up is often an afterthought at the end of a project. Because of the specificities of CSE, including it being a controversial topic in many contexts and a topic on the fringe between health, education and youth, a specific scaling approach to CSE is needed. The commentary presents a practical framework to support civil society organisations (CSOs), to address barriers to scaling up CSE in their contexts. The utilization and relevance of the framework is demonstrated in this article, by featuring examples from the scale up process of CSE in Indonesia. The framework identifies key principles for scaling up, including: taking a scaling mindset from the start, government ownership and political commitment for scale-up, and identifying the added value of CSOs. The framework starts with a self-assessment by the CSO and then follows four phases: making the case, engaging in dialogue, establishing building blocks and implementation and scale-up. Each of these phases are illustrated with examples from Indonesia.This framework is a call to action with practical guidelines to support CSOs to take on this role, because with the right scaling strategies, the largest generation of young people ever alive can become healthy, empowered and productive adults.
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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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".