Epistemic Injustice: Towards Uncovering Knowledge of Bisexual Realities in Social Work Research
Bibliographic record
Abstract
Lesbian, gay, bisexual, transgender and queer (LGBTQ) individuals experience health risks, with bisexuals experiencing higher levels of health risk compared to heterosexuals, gays and lesbians. These disparities are often attributed to stressors related to minority status. While similarities among LGBTQ experiences exist, it is plausible that bisexuals experience unique forms of marginalization, which may help explain the documented health disparities. Bostwick and Hequembourg highlight unique forms of marginalization that bisexuals experience vis-a`-vis microagressions, falling within the realm of the epistemic. Fricker’s work on epistemic injustice emphasizes marginalization particularly as it is related to knowledge and experience. Drawing on this scholarship, this paper provides a review of existing literature on the bisexual experience, and a discussion to provide a critical lens on bisexual marginalization in society and the minimal attention received in social work research. Approaches to increase bisexual visibility and attention in social work research will be discussed. Some approaches include: developing a queer theoretical perspective in practice and research to allow for greater problematization of social categories; and making a concerted effort to promote research that is inclusive of minority populations within the sexual and gender minority population group. This might include groups with intersecting points of marginalization, such as racialized and gender diverse individuals.
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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 | 0.071 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.032 | 0.038 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".