A Multiliteracies Approach to Chinese Gay Men's Socialization on Dating App: An Autoethnography
Bibliographic record
Abstract
This autoethnographic study aims to explore the pragmatic competence for Chinese gay men to navigate a dating app and scrutinize the power relations infused in the virtual sociolinguistic world, and thus inform more equitable approaches in literacy education. In this research, I draw on theories of ‘multiliteracies’ and ‘forms of capital’ to investigate how gay dating app users utilize a wide range of semiotic resources to socialize with each other and construct identities in the unequitable online discourses. Within a span of four weeks, I wrote up seven pieces of personal diaries as retrospective fieldnotes, which documented my encounters with multiple forms of ‘gay literacies’. Drawing on critical discourse analysis, I coded selected data in a thematic pattern (Creswell, 2012) and analyzed five incidents where I gradually acquired essential multiliteracies skills and became aware of the power asymmetries embedded in the online social world. Findings of this research suggest that one needs to acquire multidimensional literacy skills to access, self-portray, and have conversations on gay dating apps. In the meantime, it also indicates that various kinds of social actions performed in the online world reflect and construct power imbalance between members of different age groups and racial identities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".