A Child Rights and Social Justice Framework for Analyzing Public Policy
Bibliographic record
Abstract
Human papilloma virus (HPV) is the most common sexually transmitted infection (STI) in the United States (U.S.). Despite data that supports HPV vaccine as an effective measure to prevent anogenital cancers, vaccine uptake rates in the U.S. have stagnated over the past few years and only one third of adolescents are fully immunized. Adolescents are able to independently access STI diagnosis and treatment in all fifty states and the District of Columbia. However, only California allows adolescents to obtain HPV vaccine without parental consent. This creates a paradox where youth are able to independently receive treatment for HPV infection but not for its prevention. Current approaches to HPV vaccine education and delivery have not been successful at improving immunization rates. In this paper we propose the implementation of a child rights, social justice, and health equity-based approach to frame HPV vaccine policy. Such an approach to vaccine policy will promote children’s participation in medical decision-making. We postulate that by empowering children to be involved in issues pertaining to their health and well-being, they will be more likely to discuss HPV with their peers or families, and potentially be able to make informed independent decisions related to HPV vaccine.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".