More than Meets the Eye: Understanding Perceptions of China Beyond the Favorable–Unfavorable Dichotomy
Bibliographic record
Abstract
How is China viewed by citizens of other countries? Popular polling data based on the feeling thermometer scale can reveal overall patterns of public sentiment toward China, but they do not necessarily capture the multidimensional preferences of the public. This article takes a deeper dive into a series of surveys conducted in Canada that covered a wide range of topics, from trade and investment to international leadership. Two broad conclusions follow. First, public perceptions of China are much more nuanced and conflicted than can be quickly gleaned from the simple dichotomy of "favorable versus unfavorable," especially as one moves from overall impressions to more specific policy issues. Second, misperceptions of China are widespread and may be difficult to overcome, especially among those who already view China negatively. At a time when countries around the world are grappling with the rise of China and its expanding global footprint, failure to account for these features in public opinion about China may lead to misguided policies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12116-021-09320-1.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".