Conceptualization of Morphological Roots in Arabic and English: A Contrastive Analysis
Bibliographic record
Abstract
This paper aimed to build a contrastive orientation of notions of morphological roots in Arabic and English. The basic question addressed in this paper is whether there exist common properties of the root in Arabic and English.Belonging to two different language families, the two languages’ morphological systems are at extremes, creating challenges for interested learners and researchers. The contrastive analysis method was adopted. This analysis could provide data about the similarities and differences of the root’s structure, which can be used to meet the current study’s goals.A general contrast of the morphological system of the two languages was provided, focusing on the structure of the entity of the root in those languages. The roots’ morphological properties were contrasted to reveal and identify differences and similarities between the two languages.Findings revealed that a root in Arabic is a general abstraction, whereas in English, it is a concrete language item. There were few instances of resemblance of morphological behavior.Exploring the similarities and differences between the two languages would provide important pedagogical implications. It could also highlight potential language learning and teaching difficulties.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".