Assessing a touchy subject: The problem of evaluating sex education then and now
Bibliographic record
Abstract
Abstract Assessment is a necessary task in all areas of education, but there is no agreement on how to assess the impacts of different approaches to sex education, both on an individual level and on a population level over time. The history of mid-20th Century Family Life Education in the United States illuminates some of the obstacles that have made assessing sex education programmes so difficult: control groups, access to large numbers of research subjects and the means to verify self-reporting are elusive. These persistent challenges have to do with the nature of the subject, which is, in contrast to most subjects, not supposed to be practised at school. Standards of reliability, validity and classroom authenticity, therefore, apply partially at best. We argue that some approaches to sex education are valuable whether or not they are assessable, and that some things that are assessable may not be valuable in the way they are thought to be.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".