Bibliographic record
Abstract
This paper focuses on the challenges and solutions faced by LGBTQ (Lesbian, Gay, Bisexual,Transgender and Queer) youth. First, Trevor is used as an example to describe the experiences of minority youth, presenting the injustices suffered by Trevor as a TGNB, again extending from specific people to the LGBTQ community. The importance of minority self-identification is then presented, pointing out the important role of schools and the current situation where minorities are more vulnerable to bullying in schools and presenting solutions Asplund and Ordway propose the SCEARE (School Counselors: Educate, Affirm, Respond, and Empower) model to help LGBTQ youth on four levels: level one is education, level two is adults who provide support to the LGBTQ community, the third level is a response plan to prevent bullying in schools, and finally, the fourth level is student empowerment access to equal treatment. The goal is to improve the mental health of LGBTQ youth so that they can grow without prejudice.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".