Deductive, Inductive, and Quasi-Inductive Writing Styles in Persian and English: Evidence from Media Discourse
Bibliographic record
Abstract
This study intends to locate the place topic sentence(s) in popular Persian and English newspaper editorials, and, then, check them in terms of their paragraph organization of deduction, induction, and quasi-induction. For the purpose of the study, 98 editorials (49 for each language) were given to four specialist raters to determine the exact place of the topic sentence in the corpora. A two-way chi-square was run for the whole data and a set of one-way chi-squares for the comparison of the individual subcategories in the study. The results revealed that Persian writing is different from that of English regarding the inductive and quasi-inductive writing styles and the number of the topic sentence(s) in each editorial. However, the two languages are similar in the use of the deductive writing style. Furthermore, Persian writers prefer to develop their editorials quasi-inductively while English writers prefer to use the inductive style and rarely develop their paragraphs quasi-inductively. These writing preferences imply the existence of cross-cultural differences between the two languages.Key words: Contrastive rhetoric; Topic sentence; Writing styles; Deduction; induction; Quasi-induction
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".