“We Can Do Better”: Developing Attitudinal Scales Relevant to LGBTQ2S+ Issues—A Primer on Best Practice Recommendations for Beginners in Scale Development
Bibliographic record
Abstract
In this primer, following best practice recommendations and drawing upon their own expertise in psychometrics, the authors provide a step-by-step guide for developing measures relevant to sexual- and gender-marginalized persons (SGMPs). To ensure that readers operate from a uniform understanding, definitions for central elements of psychometric testing (e.g., reliability and validity) are provided. Then, detailed information is given about developing and refining scale items. Strategies designed to reduce a pool of items to a manageable number are also highlighted. The authors conclude this primer by discussing various forms of validation (e.g., convergent, discriminant, and known groups). To further readers' understanding, illustrative examples from measures designed for SGMPs are brought into focus throughout.
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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 | 0.108 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".