Bibliographic record
Abstract
Sex education typically claims to be value free. The focus of attention in this article is that sex education represents an extraordinary “teachable moment” for helping students consider the qualia of human engagements at a multiplicity of levels. Qualia is a term for the feel and hence the value of experience. Learning about the process of copulating machinery reveals little about the “feel” of sexual experience. Sex education should address issues students will continue to confront for the rest of their lives. Typically, students seem to waffle their way through sexually relevant encounters. Allure and fear are relevant emotions students should be mindful of when considering socio-sexual engagements of any kind. Consequently, rather than focus exclusively on sexual behavior and its consequences, educators should focus on what I have previously introduced as socio-sexual education. Socio-sexual education involves game-theoretic considerations but goes further than mere cost/benefit analysis. Socio-sexual education should focus student attention on understanding of sex and social engagements generally. People live in and through their experiences and not as mere spectators of some narrative in which experience is written about. Learning to understand socio-sexual experiences allows subsequent social and sexual adjustments for improving lived experience over a lifetime. Sex education then should broaden to socio-sexual instruction and reflection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".