“Stuck in, Can’t Come Out”: Physical Distancing and YouTube Spaces for LGBTQ Adolescents’ Wellbeing
Bibliographic record
Abstract
The pandemic has led to physical distancing measures put in place to minimize the spread of COVID-19, leading schools and community centers to close their physical locations. For many lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals living with their family, their home can be considered harmful and unsupportive due to their family’s rejection towards LGBTQ identity. LGBTQ YouTubers can be a unique avenue to understand how they can use their social media presence to act as an online supportive system that parallel traditional offline supportive systems during the pandemic where offline supportive spaces are limited. The purpose of the study is to analyze online video platforms (ie. YouTube) and whether LGBTQ YouTubers can act as support systems for their LGBTQ community during the pandemic through video content creation. Four LGBTQ YouTubers that have created videos related to the COVID-19 outbreak were chosen and their video comments (NVideo1 = 147; NVideo2 = 195; NVideo3 = 238; NVideo4 = 79) were analyzed through content analysis. Results revealed the following categories: 1) Community support and engagement between the community and the YouTuber, 2) diverse coping strategies as a result of COVID-19, 3) community’s emotional experiences surrounding COVID-19, 4) community members’ use of diverse support types, 5) YouTuber authenticity and relatability to community, and 6) intersectional identity (e.g., disability, ethnicity) experiences. Emerging themes suggest LGBTQ YouTubers can be informal online social support systems that can parallel the physical connections and support lost due to COVID-19.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".