Saudi Scholars' Heritage Language and Their Ethno-Cultural Identity in Multilingual Communities: An Exploratory Case Study
Bibliographic record
Abstract
Immigrant students’ linguistic, cultural and ethnic diversity is considered an issue of significance that speaks to the need for more rigorous research, especially in multicultural and multilingual societies. This paper highlights Saudi scholars’ heritage language and the relationship thereof to their ethnic and culture identity and its maintenance dynamics in a multilingual and multicultural society.Employing a case study approach and interviews, the researcher sought to identify the impact of Saudi scholars’ ethno-cultural identity on their heritage language. Analysis of data revealed three broad themes that emerged from the interviews: The first theme indicated that participants of both genders developed a dual cultural identity. The second theme indicated that the proficiency of Arabian scholars and their offspring had a strong impact on the ethno-cultural identity of the parents and the children born and raised in the USA. Third, negative stereotypes could be a potential cause for cultural identity clash. These broad themes seemed to be incongruous with the intricacies identified with cultural and ethnic identity maintenance mechanisms and their impact on heritage language speakers. The results have been discussed with relevance to prior studies and the theoretical framework.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".