Arabicization of Business Terms from Terminology Planning Perspective
Bibliographic record
Abstract
The main purpose of this study was to measure the extent of acceptability of Arabicized business terms. The present study investigated the attitudes of business students toward the Arabicization of business terms. Besides, it drew attention to the criteria of acceptability to be taken into consideration in the Arabicization process to produce acceptable business terms. Finally, it brought into focus the role of gender, university affiliation, and specialization in the Arabicization process of business terms. A total of two hundred questionnaires were distributed to business students at the University of Jordan and Yarmouk University. It has been found that Arabicized business terms were moderately accepted by the users. Overall, users’ attitude toward Arabicized business terms was somewhat positive. Gender and university affiliation variables had influence on these criteria. Like the specialization variable, they caused different attitudes toward these terms. Enthusiasm toward the idea of Arabicization because of pan-Arab identity was strong. This study could be useful for Arabicization decision makers to get acceptable Arabicized business terms. It is the first step towards enhancing understanding of gender role in the Arabicization process. This study also has implications for further research into the importance of Arab nationalism in promoting Arabicized terms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".