LGBTQ+ People’s Perceptions of Interactions with Outgroup Members: Implications for Social Work Education and Practice
Bibliographic record
Abstract
Abstract Engaging respectfully with individuals who identify as lesbian, gay, bisexual, transgender, queer, genderqueer, or gender-nonconforming (LGBTQ+) is an ethical imperative. To help social workers fulfill this obligation, this study explored the lived experiences of LGBTQ+ people regarding their interactions with people outside the LGBTQ+ community, and what LGBTQ+ people desire for future interactions. Narrative interviews were conducted with 11 LGBTQ+ social work graduate students and alumni to explore (a) their self-reported experiences engaging in one-on-one and small group interactions with outgroup members throughout their lives and (b) the meaning they gleaned from their life experiences across various domains (e.g., work, family, education) that might improve future interactions. Analysis produced six storylines that can be summarized as (1) unbalanced depictions of difference, (2) avoiding interactions, (3) managing positive treatment of an identity, (4) restricting certain perspectives, (5) focusing on differences, and (6) the importance of seeing humanity. The findings suggest several strategies for interacting with LGBTQ+ people in a more culturally sensitive way including discussing the LGBTQ+ community in a nuanced manner (as opposed to monolithically) and considering the level of attention paid to individuals’ sexual orientation and gender identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".