A Corpus-Based Study of Hypotactic and Paratactic Thematic Relations in English and Urdu Clause Complexes
Bibliographic record
Abstract
The present research inquires the paratactic and hypotactic thematic relations in terms of their grammatical realization, functional significance (Halliday, 1994) and thematic progression (McCabe, 1999). In the paratactic clause complexes, two or more independent clauses are joined by the coordinating conjunctions while in the hypotactic clause complexes, two or more independent and dependent clauses are joined by the subordinating conjunctions. The specific objectives of this research are: (1) to define the grammatical realization of paratactic and hypotactic thematic structures in the English and the Urdu texts, (2) to describe the functional significance of paratactic and hypotactic thematic structures particular to information flow and thematic progression in the English and the Urdu texts, and (3) to discuss how effectively the paratactic and hypotactic thematic structures in the English text have been translated into the Urdu text. The English text, Things Fall Apart by Chinua Achebe and its translated Urdu text, Bikharti Duniya by Ikram Ullah have been selected for this study. These texts have been annotated through the annotation scheme of UAM Corpus Tool (O’Donnell, 2008). The results reveal that the Urdu text uses multiple equivalents of conjunction either paratactic or hypotactic in the English text. Thematic progression patterns in both texts are mostly constant, linear and peripheral. The unmotivated displacement of paratactic and hypotactic themes causes ambiguity and change the information flow in the Urdu text. The present research is significant to support the systemic functional grammar of Urdu taking into account of English.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".