Digital microaggressions and queer youth: incidence, perceived impacts, and practice implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it