Frequency of Using Najdi Arabic Words Among Saudi College Male Students
Bibliographic record
Abstract
The study of dialects may be subsumed under the very broad rubric of colloquialism which comes at the bottom of the formality versus informality scale. We focus on the Najd dialect perception, as the central dialect in Saudi Arabia, among Saudi male college students. By conducting two experiments, questionnaires and follow-up semi-structured interviews, on 137 male students, user-based frequencies of the topper 50 Najdi words are generated. The second phase aims at semantically categorizing the topper content words so that conclusions can be drawn about the inclination of using Najdi words among the college students. Results show that the categorization of the retrieved 50 Najdi words, according to the part of speech, demonstrates that the most applauded Najdi Arabic words are verbs and adjectives. Synonyms are even retrievable from this method of compilation. Nouns are the most resistant part of speech at the morphological level.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".