Bibliographic record
Abstract
The present study is a contrastive analysis of the notion of Ergativity in English1 and Arabic2. It attempts to discuss this phenomenon to find out the points of similarity and difference between the two languages in this particular linguistic area. It offers an explanation and a detailed description of the term and illustrates the various types and forms of verbs that can be handled under the headings ergative verbs and non-ergative verbs showing how the former differ from the latter. Additionally, it investigates the verbs which are used both transitively and intransitively in the two languages. All these types of verbs will be identified, classified, and analyzed according to the Quirk grammar - the approach to grammatical description pioneered by Randolph Quirk and his associates, and published in a series of reference grammars during the 1970s and 1980s, notably A Grammar of Contemporary English (1972) and its successor A Comprehensive Grammar of the English Language in1985. Reference, will, however, be made, wherever necessary, to the principles, techniques and terminology of other models of grammar. The method is, thus, more or less, eclectic. As far as ergativity in Arabic is concerned, the study adopts the model of grammatical description and classification pioneered by traditional Arab grammarians such as Siibawayhi, Ibn ‘Aqiil, and Mubarrid, and by modern Arab grammarians like Ghalaayiinii, ‘Udhaymah, and ‘Abbaas Hasan, among others. The conclusion part offers the main findings of the study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".