Constructing inclusivity: how state laws and local community contexts shape LGBTQ+ student inclusion and belonging in schools
Bibliographic record
Abstract
Though lesbian, gay, bisexual, transgender, and queer (LGBTQ+) youth face increased risk for mental health problems, research suggests that protective state-level legislation and school-level supports can reduce this risk. However, the effectiveness of state laws and school policies may depend on how they are enacted in schools, and particularly whether they are contested by local communities. To examine this idea, we compare ethnographic and interview data from 2019-2023 from two Colorado public school districts in different community contexts. We find that despite shared state laws, these districts construct LGBTQ+ inclusivity differently and that this difference is shaped, in part, by community politics. In the more liberal Ensley Public Schools, protective state laws are integrated into district policy and practices, and school staff are intentional and consistent about creating inclusivity for LGBTQ+ students. In the more conservative Field Public Schools, we find similar district policies, but staff are more constrained in cultivating LGBTQ+ inclusion due to community attitudes that contest support for LGBTQ+ students. We leverage these findings to consider how external community contexts shape the experience of state laws and associated policies for LGBTQ+ youth in schools, with implications for sociological theory and policy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".