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Record W2329358264 · doi:10.1093/swr/svw005

Pitfalls, Potentials, and Ethics of Online Survey Research: LGBTQ and Other Marginalized and Hard-to-Access Youths

2016· article· en· W2329358264 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Work Research · 2016
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransgenderLesbianQueerPopulationSociologySexual minorityPsychologyGender studies

Abstract

fetched live from OpenAlex

Online research methodologies may serve as an important mechanism for population-focused data collection in social work research. Online surveys have become increasingly prevalent in research inquiries with young people and have been acknowledged for their potential in investigating understudied and marginalized populations and subpopulations, permitting increased access to communities that tend to be less visible-and thus often less studied-in offline contexts. Lesbian, gay, bisexual, transgender, and queer (LGBTQ) young people are a socially stigmatized, yet digitally active, youth population whose participation in online surveys has been previously addressed in the literature. Many of the opportunities and challenges of online survey research identified with LGBTQ youths may be highly relevant to other populations of marginalized and hard-to-access young people, who are likely present in significant numbers in the online environment (for example, ethnoracialized youths and low-income youths). In this article, the utility of online survey methods with marginalized young people is discussed, and recommendations for social work research are provided.

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.

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)0.016
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.125
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.636
GPT teacher head0.604
Teacher spread0.032 · 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