The Same Oh, But Different Meanings in Shopaholic & Sister and Its Thai-Translated Version
Bibliographic record
Abstract
This paper identifies the broad spectrum of feelings expressed by the primary interjection oh and the range of procedures that Ploy Chariyaves used to render them into Thai from a widely-read chick lit novel, Shopaholic & Sister written by Sophie Kinsella. To carry out the study, the types of feelings proposed by Drzazga (2021) are used to analyze a total of 190 instances of oh. The analysis reveals that the interjection provides an extensive array of 25 feelings that includes fear, recognition, uncertainty, surprise, nervousness, shock, excitement, disappointment, embarrassment, continuation, awareness, happiness, relaxation, negation, preceding description, certainty, dissatisfaction, sympathy, annoyance, response, preceding greeting, satisfaction, disagreement, sadness, and pain. Furthermore, the translation strategies recommended by Boonterm (2019) are adopted to consider how the instances of oh are translated are adopted to consider. The consideration shows that the translator employs five main translation procedures, namely primary interjection, omission, interjection phrase, secondary interjection, and question. More specifically, the procedure used most frequently is translating into primary interjection making up 90 percent. The findings suggest that conveying various emotions through the instances and retaining the primary interjection in the target text enable the story to be delightful and grab readers' attention.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".