Digital microaggressions and queer youth: incidence, perceived impacts, and practice implications
Bibliographic record
Abstract
Queer youth frequently go online to meet their developmental and socialization needs. Digital forms of violence may impact these opportunities, though research in this area remains inadequate. This study examines experiences with digital microaggressions for 1,804 queer youth aged 14–24 across three countries. Respondents to a mixed-methods online survey shared the frequency and perceived impacts of their encounters with anti-queer digital microaggressions directed specifically at them, as well as indirectly witnessed by them while online. Overall, youth reported the near ubiquity of anti-queer microaggressions in their digital contexts. Almost all also indicated anti-queer digital microaggressions directed specifically at them at least somewhat affected their emotional well-being (94%). Harmful physical and behavioral health impacts were also reported. Importantly, these direct and indirect experiences impacted youths’ feelings about being queer and talking about being queer. Notably, most participants also believed they grew in positive ways from being a direct target of digital microaggressions (90%). However, few queer youth reported trusting adult professionals (e.g., school counselors, teachers) to help them with these experiences. The pervasiveness and cumulative influence of digital microaggressions in queer youths’ online contexts may have immediate and longer-term impacts. Implications for future research and professional practice are discussed.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".