Development and validation of the SAFE (Socially Ascribed intersectional identities For Equity) questionnaire
Bibliographic record
Abstract
Traditional questionnaires do not capture the complexity of how people are viewed by others and grouped into categories on the basis of what is inferred (or not) about them. This is critical in applying an intersectionality framework in research because people are negatively impacted because of "who they are" but also based on "how others see them." The purpose of this project was to develop and validate a questionnaire, grounded in intersectionality theory and a nuanced understanding of social position, that can be applied in large-scale, population-based surveys and studies. Drawing on 61 existing quantitative surveys collecting identity-based information and 197 qualitative studies on intersectionality describing the complex ways in which people's social positions are constructed and experienced, we created a draft questionnaire comprising five parts: 1) Sex and Gender, 2) Sexuality and Sexual Orientation, 3) Cultural Context, 4) Disability, Health, and Physical Characteristics, and 5) Socioeconomic Status. A draft of the questionnaire was then reviewed by experts via the Delphi process, which gauged the accessibility of the questionnaire (e.g., language used, length) and the relevance of its content using a 5-point scale and open-ended questions. These responses were ranked, analyzed, and synthesized to refine the questionnaire and, ultimately, to obtain ≥75 % consensus on each questionnaire item and response option. The SAFE questionnaire provides an opportunity to take a significant step forward in advancing our understanding of the complex, intersectional nature of social participation and marginalization.
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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 | 0.078 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".