Shanzhai-ed Didi and the “New Chinatown”: WeChat-based ride-hailing among Chinese international students in Metro Vancouver
Bibliographic record
Abstract
This thesis examines the role WeChat plays in the life experience of Chinese international students in Metro Vancouver, Canada, focusing on the use and development of ride-hailing platforms from July to November 2018. By following WeChat-based underground ride-hailing using multi-sited ethnography (Marcus, 1995) and interviewing students working as drivers and using these services, this thesis conceptualize WeChat as an assemblage (Slack, 2012) that combines infrastructures, networks, ideas and spaces, rather than another imported social media application hindering their acculturation. This thesis examines students’ economic and social practices in replicating a digitally-connected “Chinese” lifestyle in Canada through “shanzhai-ed” platforms on WeChat, which are shaped and restricted by local media discourses and regulations, including BC’s long-existing yellow peril discourse (Deer, 2006). Examining ride-hailing as part of the assemblage, this thesis showcases the entanglement of these students’ lives with technologies, social networks, labour and spaces in the local negative discursive and regulatory environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".