English Passive and the Function of Shodan in Persian
Bibliographic record
Abstract
Although English and Persian share a basic structure in the formation of passive with the help of past participle of the main verb accompanied by “be” in English and “shodan” (i.e. become) in Persian on a syntactic basis, Persian resorts to morphological alternation, too. However, the verb shodan is not merely an auxiliary verb; it can serve as a main verb as well. In fact, Persian passives have various overlapping realizations, bearing the meaning of “possibility” and “going”, too. This study also sought out to investigate whether or not passives differed in written texts. In addition to library resources, the study made use of a comparative text analysis approach to investigate the application of passive structures in the novel Animal Farm (Orwell, 1956) and its Persian translation (Akhondi, 2004). The results revealed that the conventional function defined for Persian shodan as an auxiliary verb for passive structure outnumbers the passive verbs used in the original novel. Findings also indicate that passive voice is not limited to one form of shodan and past participle; in fact, transitivity alternation plays a key role, too. In addition, the combination of shodan with nouns or adjectives is twofold: it can produce both active and passive voice structures. This study was targeted at EFL learners and teachers as well as translators who will duly be provided with fundamental awareness when dealing with English and Persian in learning / teaching process and translating from English into Persian and vice versa.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| 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".