Chinese and English Reviews of a Story about Teenagers’ Struggles: <i>A Multi-method Analysis of Cultural Differences in Narrative Interpretation</i>
Bibliographic record
Abstract
Wonder, a fictional story told both in print (2/2012) and movie (11/2017) formats, depicts a middle-school student, Auggie, who struggles with social exclusion and being the target of school based relational bullying due to his genetically based facial deformity. Internationally popular, abundant everyday reviews by non-professional commenter are easily accessible on-line, e.g., on Douban (Chinese) and Common-Sense Media (English). Access to these comments enables the investigation of what perspectives (knowledge and understanding, beliefs and opinions, etc.) reviewers draw upon in sharing their thoughts about the protagonist's struggles. In this study we undertook two analyses. In analysis 1, we asked, how do Chinese-/English-speaking reviewers comment on Auggie's struggles to be accepted by his peers at school, both the causes of his social problem and the ways Auggie dealt (or might deal) with his situation. In analysis 2, we explored comments that reviewers made about Auggie's only sibling, his elder sister, Via, a first-year high school students who, in the story, struggles to find a balance between meeting her own need for her parents’ love and attention, and fulfilling her familial role and obligations, given Auggie's special needs. Based on data from open-source “every day” books/movies review websites, we compared, culturally, the perspectives on the story expressed by Chinese and English commenters through emic (both thematic and discursive) exploratory analyses. Analysis 1 suggests that comparatively, Chinese-speaking reviewers tend to more often focus on the value of solving the problem, and in doing so, articulating the corresponding ways a victim of bullying or neglect can be or become both strong and interdependent, whereas English-speaking reviewers more often tend to emphasize exploring and understanding the causes of the relational problems and tend to suggest independence-oriented ways of solving the social problem by changing the actions of others. Analysis 2 suggests differences across Chinese-and English-speaking reviewers in perceptions of and judgements about the fundamental “culture based” determinants and ways for Via to deal with her relational struggle within the family.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".