Methods in HIV-Related Intersectional Stigma Research: Core Elements and Opportunities
Bibliographic record
Abstract
Researchers are increasingly recognizing the importance of studying and addressing intersectional stigma within the field of HIV. Yet, researchers have, arguably, struggled to operationalize intersectional stigma. To ensure that future research and methodological innovation is guided by frameworks from which this area of inquiry has arisen, we propose a series of core elements for future HIV-related intersectional stigma research. These core elements include multidimensional, multilevel, multidirectional, and action-oriented methods that sharpen focus on, and aim to transform, interlocking and reinforcing systems of oppression. We further identify opportunities for advancing HIV-related intersectional stigma research, including reducing barriers to and strengthening investments in resources, building capacity to engage in research and implementation of interventions, and creating meaningful pathways for HIV-related intersectional stigma research to produce structural change. Ultimately, the expected payoff for incorporating these core elements is a body of HIV-related intersectional stigma research that is both better aligned with the transformative potential of intersectionality and better positioned to achieve the goals of Ending the HIV Epidemic in the United States and globally. (Am J Public Health. 2022;112(S4):S413–S419. https://doi.org/10.2105/AJPH.2021.306710 )
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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 | 0.374 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".