Romantic relationships and sexuality in diverse adolescent populations: Introduction to the special issue
Bibliographic record
Abstract
Although scientific research on adolescent romantic and sexual development has proliferated in recent years, currently, too little is known about how development in these areas can be understood across diverse populations (e.g., different socio-cultural groups within countries) and contexts (e.g., countries or different proximal social environments). The goal of the current virtual special issue in the Journal of Adolescence was to highlight relevant and timely empirical findings from studies utilizing innovative and diverse research methods in the areas of adolescent romantic and sexual development from around the globe, with an emphasis on data collected outside of the Western world. It combines an interesting set of nine empirical papers, which describe datasets from 5 countries (Canada, India, Indonesia, Mexico, and the United States). In this editorial, we provide an introduction to this special issue, and illustrate how these studies expand our understanding of adolescent romantic and sexual development by examining: 1) romantic and sexual relationship constructs that are relevant for understudied and diverse populations; 2) how culture-specific factors may shape adolescents' romantic and sexual relationships; 3) how romantic and sexual relationship constructs are linked to psychosocial adjustment outcomes in understudied cultural contexts; 4) the role of different proximal social environments (e.g., parents, siblings, peers) in romantic and sexual development in diverse populations.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".