Chasing the rainbow: lesbian, gay, bisexual, transgender and queer youth and pride semiotics
Bibliographic record
Abstract
While the pride rainbow has been part of political and social intervention for decades, few have researched how lesbian, gay, bisexual, transgender and queer young people perceive and use the symbol. How do lesbian, gay, bisexual, transgender and queer youth who experience greater feelings of isolation and discrimination than heterosexual youth recognise and deploy the symbol? As part of a larger study on supportive lesbian, gay, bisexual, transgender and queer youth environments, we conducted 66 go-along interviews with lesbian, gay, bisexual, transgender and queer youth people from Massachusetts, Minnesota and British Columbia. During interviews, young people identified visible symbols of support, including recognition and the use of the pride rainbow. A semiotic analysis reveals that young people use the rainbow to construct meanings related to affiliation and positive feelings about themselves, different communities and their futures. Constructed and shared meanings help make the symbol a useful tool for navigating social and physical surroundings. As part of this process, however, young people also recognize that there are limits to the symbolism; it is useful for navigation but its display does not always guarantee supportive places and people. Thus, the pride rainbow connotes safety and support, but using it as a tool for navigation is a learned activity that requires caution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".