Bibliographic record
Abstract
Through in-depth interviews, this chapter examines the ways 25 LGB young adults (18-35 years old) used digital technologies as they do emotion work to preserve relationships with heterosexual parents. Findings demonstrate that, with the aid of technology (especially texting, Skyping, social media, YouTube, television, and various informational websites), LGB young adults engaged in personal and interpersonal forms of “preventive” and “palliative” emotion work. The former's aim was to prevent noxious feelings and the latter to preserve familial relationships despite emotional pain. These forms of emotion work allowed LGBs to maintain relationships with their parents, but by privileging the emotional wellbeing of heterosexual parents above those of LGBs. The authors conclude by suggesting that digital technology can be a dual-edged sword. Access to these technologies may allow LGBs to connect with queer communities and to obtain information about queerness, yet utilizing these technologies as a way to preserve familial relationships was an adaptation to--rather than disruption of--heterosexism and homophobia.
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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 | 0.000 | 0.001 |
| 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 | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".