Bibliographic record
Abstract
Background: Sexual consent remains one of the most important tools in the prevention of sexual violence, for which adolescents are an especially vulnerable group. However, it is unclear how sexual consent processes are defined and used by this population. To bridge this gap in knowledge, we present a protocol for a forthcoming scoping review that will identify and synthesize the available empirical research findings on sexual consent conceptualizations and processes among adolescents. Methods/Design: Using the framework by Arksey and O’Malley (2005), a systematic search of six academic databases (Education Source, ERIC, Gender Studies Database, PsycINFO, Social Services Abstracts, and Sociological Abstracts) will be conducted; this range has been selected due to the multi-disciplinary nature of sexual consent research. Following two levels of screening, data from the full-text articles will be charted and subjected to qualitative thematic analysis. Discussion: These collated results will provide a map of key concepts and establish gaps in the extant literature in order to guide future research on this topic. The findings will advance our knowledge of sexual consent as it is understood by the adolescent population; they may also inform the content and delivery of sexual education programs to ensure that they are relevant to their target audience and assist in the prevention of sexual violence.
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 | 0.049 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".