Development of the nonbinary gender microaggressions (NBGM) scale
Bibliographic record
Abstract
Background: While research pertaining to nonbinary microaggressions has become increasingly comprehensive in recent years, a measure specifically assessing this construct does not yet exist.Aims: The purpose of the present research was to develop and validate the Nonbinary Gender Microaggressions (NBGM) scale, which will allow future researchers to quantitatively examine nonbinary individuals’ experiences of microaggressions. Methods and Results: In Study 1 (n = 5), interviews with nonbinary individuals were conducted to explore their microaggressive experiences. The results of this study, as well as findings from previous qualitative research, were used to generate an initial pool of 92 items. In Study 2 (n = 158), a principal component analysis, which was used for item reduction, resulted in the retention of 41 items. In Studies 3 (n = 151) and 4 (n = 266), an exploratory factor analysis yielded a 23-item 5-factor solution (i.e., Negation of Identity [6 items], Inauthenticity [6 items], Deadnaming [4 items], Trans Exclusion [3 items], and Misuse of Gendered Terminology [4 items]), and a confirmatory factor analysis found that this solution demonstrates adequate model fit. Evidence of the measure’s scale score reliability, convergent validity, and incremental validity also were provided. Discussion: These findings indicate that, overall, the NBGM scale is a psychometrically sound measure of nonbinary individuals’ experiences of microaggressions. As such, this measure can be utilized by future researchers and clinicians to better understand nonbinary individuals’ microaggressive experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".