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Adolescent online romantic relationship initiation: Differences by sexual and gender identification

2015· article· en· W1995879943 on OpenAlexaff
Josephine D. Korchmaros, Michele L. Ybarra, Kimberly J. Mitchell

Bibliographic record

VenueJournal of Adolescence · 2015
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsTellabs (Canada)
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsPsychologyLesbianPopularityRomanceTransgenderQueerDevelopmental psychologyHuman sexualityThe InternetSocial psychologyGender studiesSociology

Abstract

fetched live from OpenAlex

Data from the national Teen Health and Technology Study of adolescents 13-18 years old (N = 5091) were used to examine online formation of romantic relationships. Results show that lesbian, gay, bisexual, transgender, and queer (LGBTQ) and non-LGBTQ adolescents similarly were most likely to have met their most recent boy/girlfriend in the past 12 months at school. However, they differed on many characteristics of romantic relationship initiation, including the extent to which they initiated romantic relationships online. LGBTQ and non-LGBTQ adolescents also differed on level of offline access to potential partners, offline popularity, and numerous other factors possibly related to online relationship initiation (e.g., Internet use and demographic factors). Even after adjusting for differences in these factors, LGBTQ adolescents were more likely than non-LGBTQ adolescents to find boy/girlfriends online in the past 12 months. The results support the rich-get-richer hypothesis as well as the social compensation hypothesis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.166
GPT teacher head0.381
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2015
Admission routes1
Has abstractyes

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