Narrative processing of anti-LGBTQ+ victimisation: exploring themes of redemption and meaning
Bibliographic record
Abstract
Despite increasing social acceptance, anti-LGBTQ+ victimisation remains a major risk to the well-being of LGBTQ+ people in Canada. Research has robustly shown that anti-LGBTQ+ victimisation is associated with indicators of negative mental health. Research is needed to better understand the ways that LGBTQ+ people adaptively cope with such victimisation experiences. Addressing this need, in the current study, 30 LGBTQ+ adults (19 to 66 years of age; M = 36.87; SD = 11.42) from a large Canadian city provided narrative reconstructions of victimisation experiences. These narratives were content analysed for themes of redemption and meaning making. Somewhat surprisingly, less than half of the sample narrated their experiences with themes of redemption and meaning. When it was present, redemption and meaning manifested in diverse ways across participants. Examples of redemptive themes included achieving a sense of justice, expressing generative concern for the welfare of other people, and finding a deeper level of self-acceptance. Examples of meaning making included lessons about personal safety, new positive and negative self-understandings, and wisdom into how to deal with life. The significance of these results is discussed within the context of psychotherapy with anti-LGBTQ+ victims.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".