De l'exploration du multilinguisme dans les villes africaines = Exploring multilingualism in African urban cities
Bibliographic record
Abstract
This paper investigates the process of language maintenance and language shift among ethnic minority groups living in Dilling city, the Nuba Mountains, and Khartoum, the capital city of Sudan. A 22-item questionnaire was used to collect data on language proficiency, language use, and language attitude. The results show that a considerable number of younger.-generation migrants have adopted Arabic as their primary language. Arabic was also used predominantly in most domains of communx'cation. Although many respondents showed a positive attitude to their ethnxc languages, they actually did not make any effort to maintain them. The analysis suggests that language shift to Arabic in Dilling xs~ more pronounced than that in Khartoum. The main reason behind this difference is that a significant portion of the sample population in Khartoum belongs to the Southern groups who proved to be bigger in size, more homogeneous, and highly proud of their ethnx'c and cultural identity. Another possible reason is that while ethnic individuals from the same groups tend to live together in certain areas in Khartoum, those in Dilling Ixve in scattered areas around the cxty. Introduction Ho\mes (2000) contends that language shift tends to be slower among communities where a minority language is highly valued. That is, a positive attitude toward a language will help its speakers make every effort to maintain it. Negative attitudes, on the other hand, may lead to Zack of such efforts and consequently accelerate the process of language shift toward the dominant language. This assumption has been confirmed by S\avik (200 ) who found a strong correlation between negative attitudes towards Maltese and rapid shift to English among Maltese migrants in Canada. Studies on language attitudes in the Sudanese context indicate that ethnic migrants in Khartoum are undergoing a significant shift to Arabic in spite of the positive attitudes they hold
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".