Chinese Canadian Perceptions of the Social Credit System
Bibliographic record
Abstract
In 2014, China announced that they would be implementing a Social Credit System (SCS) in hopes of encouraging trustworthy behavior between Chinese citizens, corporations, and government agencies (State Council, 2014). Currently, existing literature frames the SCS as a surveillance mechanism for social management and behavior engineering (Hoffman, 2018; Creemers, 2018; Dai, 2018; Langer, 2020). Additionally, research has also indicated that Chinese citizens generally hold high levels of approval towards the SCS (Kostka, 2019). Kostka (2019) further noted that because there was almost no disapproval amongst citizens, their opinions may be highly influenced by the authoritarian regime they are situated in. Therefore, this thesis explores the perceptions of Chinese Canadians regarding the SCS in hopes that this diaspora population can further shed light on Chinese public opinion towards the SCS. Using a mixed methods approach, my study incorporates a survey (n=63) which offers a quantitative snapshot of Chinese Canadian perceptions, while the use of semi-structured interviews (n=8) provides for an in-depth understanding of why they have those views. Based on this cross-sectional study, the findings suggest that older and first generation immigrants hold more favorable views of the SCS. Drawing from their own lived experiences in China and their understanding of Chinese culture, participants explained that while the SCS could be understood as an instrument for control, it also promotes trust, awareness, fairness, and a higher quality of living within a society that is plagued by fraud and distrust.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".