Helpful information to whom? An intersectional critique of the ‘it gets better project
Bibliographic record
Abstract
ABSTRACT The ‘It Gets Better Project’ started as an online campaign responding to the suicide deaths of multiple young gay men in Fall 2010. The campaign involved people posting videos providing messages of hope and helpful health information for youth as they finish high school and can move on with their lives. Although there is seemingly good intentions driving this this campaign, we were troubled by the initial positioning of the campaign. In this paper we use an intersectional critique to discuss the lack of inclusivity in these videos and how it created a potential opportunity to engage with more LGBTQ youth. Furthermore, we suggest that future online campaigns for gender/sexual minority populations be more mindful of others' lived experiences and how this may impact how they seek health information.
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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 | 0.064 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.027 | 0.161 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.026 | 0.043 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".