(Im)mobility and performance of emotions: Chinese international students’ difficult journeys to home during the COVID-19 pandemic
Bibliographic record
Abstract
This article examines mediated performances of emotions by Chinese international students in their transnational journeys returning to China during the COVID-19 pandemic with a focus on the role of mobile media in helping students cope with their cross-border (im)mobility and symbolic immobility. By thematically analyzing 36 self-representational videos produced by returning Chinese students on a burgeoning mobile media platform Douyin, we identify 5 overarching themes of emotional performance: fear, pride, gratitude, shame, and solidarity. We propose that mobile media has the potential to create a hybrid space that witnesses and elicits empathy for the hardship experienced by marginalized mobile groups during the global pandemic. Mobile media, by enabling simultaneous communication, amplifies the sensation of belonging in times of isolation and ambiguity and offers dialogic venues for disparate groups across geographical and socioemotional distances. Our findings suggest the vulnerability of mobile communities in the event of a global pandemic, and the affordances of mobile media in confronting and resolving such precarity. We call attention to the intersections of mobile communities and mobile media amid the global pandemic, particularlyon the experiences and performances of emotions in hybrid spaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".