Creating Safe and Supportive Schools for LGBTQ Students and Families: A Review of <i>Creating Safe and Supportive Learning Environments: A Guide for Working With Lesbian, Gay Bisexual, Transgender, and Questioning Youth and Families</i> and <i>Responsive School Practices to Support Lesbian, Gay, Bisexual, Transgender, and Questioning Students and Families</i>
Bibliographic record
Abstract
This essay provides a review of two resource guides for professionals working with LGBTQ youth and families: Responsive School Practices to Support Lesbian, Gay, Bisexual, Transgender, and Questioning Students and Families, a book and CD written by Emily S. Fisher and Kelly S. Kennedy, and Creating Safe and Supportive Learning Environments: A Guide for Working With Lesbian, Gay, Bisexual, Transgender, and Questioning Youth and Families, a book edited by Emily S. Fisher and Karen Komosa-Hawkins. The first book and CD are more practical in focus and part of the School-Based Practice in Action series; the second resource guide provides theoretical foundations and background knowledge about lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ) issues, as well as practical applications for schools and communities. Both resource guides are valuable, but they differ in scope and content: the first book and CD are accessible and designed for professional development purposes; the second edited book is in depth and academic in approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".