'A Tart and Bitter Feeling of Jealousy and Remorse’: Appraising Subjectivity and Cultural Dimensions in Hong Kong and Indian Readings of an English Poem
Bibliographic record
Abstract
English has become a global language. Chinese and Indian tertiary students comprise a majority of global English users, and a significant proportion of highly-proficient graduates in the knowledge economy. World Englishes authors are included in the curricula of both nations. This study analysed data from 98 Indian and 92 Hong Kong tertiary readers of Tagore’s ‘The Golden Boat’. Data was analysed using Appraisal analysis to compare subjective attitudes. Appraisal analysis uses computational methods to produce a detailed analysis of attitudes in three systems: emotion, judgment and appreciation. Content analysis was completed, using Hofstede’s cultural dimensions, to detail differences between the two groups. Cultural dimensions are a long-standing, validated research paradigm based in psychometric data, and are widely used. Examples of students’ comments explore similarities in emotional responses, and generalisations to self and others. The study indicates cultural specificities in the tendency to generalise literature to personal experience, and in specific areas of subjective attitudes. Differences were found in the specific cultural meanings used to explain these, by the two groups. Indian responses realise cultural values stressing overarching philosophical meanings, where Hong Kong responses focus on task orientation, particularly reward. Implications are noted for second-language literary readings, tertiary institutions and graduates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".