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Record W4214716002 · doi:10.1080/19361653.2022.2043802

Family nonsupport of young trans people, experiences of legal problems, and access to the legal system

2022· article· en· W4214716002 on OpenAlexaffabout
Julie James, David J. Brennan, Ryan Peck, Nicole Nussbaum

Bibliographic record

VenueJournal of LGBT Youth · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of TorontoHIV Legal NetworkToronto Metropolitan University
Fundersnot available
KeywordsLegal responsibilityPsychologyLegal researchLegislationLegal statusSociologyPolitical scienceLawGender studiesCriminologySocial psychology

Abstract

fetched live from OpenAlex

This paper explores the connections between parental nonsupport of a young person’s trans or gender diverse identity and young trans people’s experiences of justiciable legal problems11 Justiciable legal problems are defined as those matters that are capable of being settled by law or by the action of a court or an administrative tribunal. as well as access to the legal system to address these issues. Qualitative youth data were drawn from the TRANSforming JUSTICE: Trans Legal Needs Assessment Ontario (TFJ) study. The narratives of 16 young trans people (16–29 years) were analyzed to identify parental factors in relation to encountering legal problems and accessing the legal system. Five themes emerged: parental nonsupport of gender identity, identity documentation issues, accessing medical care, employment legal problems, and housing legal problems. Results indicate that parental nonsupport of a young person’s trans or gender diverse identity may render a young person more vulnerable to experiencing justiciable legal problems and may also create barriers to accessing the legal system to address these issues.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0010.001
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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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 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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of LGBT YouthSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207