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Record W3082175839 · doi:10.3138/cjhs.2020-0001

Testing an intergroup relations intervention strategy to improve children’s appraisals of gender-nonconforming peers

2020· article· en· W3082175839 on OpenAlexaffvenueabout
Laura N. MacMullin, A. Natisha Nabbijohn, Karen Man Wa Kwan, Alanna Santarossa, Diana E. Peragine, Haley J James, Wang Ivy Wong, Doug P. VanderLaan

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsIntervention (counseling)PsychologyNonconformityPsychosocialVignetteDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Past research has linked poor psychosocial adjustment among children who express gender nonconformity (GNC) to poor peer relations (e.g., facing ridicule and rejection); however, very little research has explored whether it is possible to improve children’s appraisals of GNC. The present study attempted to replicate a previous intervention that was conducted among 8- to 9-year-old children from Hong Kong that successfully improved children’s appraisals of gender-nonconforming peers. Specifically, it tested whether the same intervention was successful at improving appraisals of gender-nonconforming peers in a sample of children from Canada and among both 4- to 5-year-old and 8- to 9-year-old children. To do so, we employed an experimental vignette design among 4- to 5-year-old ( n = 176; 48% girls) and 8- to 9-year-old ( n = 182; 49% girls) children. In the intervention condition, targets were presented who displayed mostly gender-nonconforming preferences, some gender-conforming preferences, and positive attributes. Following the intervention, participants’ appraisals of gender-nonconforming and gender-conforming targets were assessed through verbal reports, a sharing task, and a rank-order task. Overall, the intervention did not improve appraisals of GNC, and there were no differences based on age or gender of the participants, or gender of the targets. We discuss possible reasons why there was a cultural difference in the effectiveness of the intervention and how future intervention work in this area might be strengthened.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.372
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2020
Admission routes3
Has abstractyes

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