A Contrastive Study on the Thematic Structure Functions of English and Arabic Short Stories
Bibliographic record
Abstract
Charles Dickens’s (1909) “Little Dorrit” and Al Tayeb Saleh’s (1997) “Nakhla Ala Al Jadwal” were the subject of this investigation. Using Halliday and Matthiessen’s (2014) model of thematic categorizations and functions, this research paper examined thematic structure functions in relation to Dickens’s and Saleh’s short stories. Hence, 234 English and 304 Arabic clauses were manually extracted from the short stories and analyzed regarding the proposed framework. The findings revealed that the use of topical themes was the highest and the interpersonal was the lowest in frequency while the textual themes were at some point in between the topical and interpersonal ones. The comparison also had no bearing on the topical themes because they were identical, but the textual and interpersonal themes recorded distinct results. In Arabic, the utilization of textual themes was higher but the implementation of interpersonal themes was more employed and exercised in English. Each of which indicates multifarious reasons and functions to be regarded to the author’s vantage point.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".