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Record W2921949078 · doi:10.1080/15299716.2019.1576153

A Systematic Review of the Psychometric Properties of Binegativity scales

2019· review· en· W2921949078 on OpenAlexaff
Melanie A. Morrison, Mark Kiss, Kandice Parker, Thomas Hamp, Todd G. Morrison

Bibliographic record

VenueJournal of Bisexuality · 2019
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologySexual orientationLesbianClinical psychologyQueerScale (ratio)Social psychology

Abstract

fetched live from OpenAlex

Research suggests that bisexual people encounter discrimination from heterosexual people, lesbian women, and gay men. This discrimination, henceforth labeled ‘binegativity,’ manifests itself in various forms such as denying and delegitimizing bisexuality as a sexual orientation, rendering bisexual persons invisible from the larger queer community, and promulgating various myths about bisexual persons (e.g., the myth that bisexual persons are intrinsically more promiscuous than their nonbisexual counterparts). Binegativity has the potential to compromise bisexual individuals’ mental and physical health and, consequently, their experiences of discrimination are being assessed using a wide variety of measures. To date, a review of the psychometric properties of the scales utilized to measure binegative experiences has not been published. To address this omission, a systematic review was completed to determine each scale’s adherence to best practices in psychometric development and testing. Forty-one studies using 30 unique scales were awarded a score from 0 to 5 (i.e., higher scores denote greater psychometric soundness). The Anti-Bisexual Experiences Scale emerged as the strongest measure of experienced binegativity. Other measures are identified and reviewed, and recommendations for future research are offered.

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)0.006
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.231
GPT teacher head0.468
Teacher spread0.237 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2019
Admission routes1
Has abstractyes

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