Advocacy for International Family Planning: What Terminology Works?
Bibliographic record
Abstract
Advocating for international family planning while avoiding miscommunications with politically and religiously conservative policy makers and the public requires care and clarity with language. We find that terms such as "international family planning" are well received when the meaning is clearly explained, such as "enabling couples to determine the number and timing of pregnancies, including the voluntary use of methods for preventing pregnancy - not including abortion - harmonious with their beliefs and values". Family planning also helps reduce abortions - a powerful message for conservative policy makers and the public. We concur with Dyer et al. (2016) that the messenger is important; we find that many of the most effective advocates are religious leaders and faith-based health providers from the Global South. They know and validate the importance of family planning for improving family health and reducing abortions in their communities. "Healthy timing and spacing of pregnancy" is positive language for policy makers, especially when describing the health impact for women and children. Universal access to contraceptive services is emerging as vital for family health and also to help achieve the Sustainable Development Goals (UN 2015). Language on international family planning will evolve, and clarity of meaning will be foundational for effective advocacy.
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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 | 0.048 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".