Toward a Sociolinguistics of Modern Sub-Saharan African South–South Migrations
Bibliographic record
Abstract
Abstract Despite their large demographic size, intra-continental African migrations have hardly been taken into account in the theorizing on migration in transnational studies and related fields. Research questions have been framed predominantly from a South-to-North perspective on population movements. This may be a consequence of the fact that the extent and complexity of modern population movements and contacts within Africa are hard to assess, owing mainly to lack of reliable data. For sociolinguists the challenge is even greater, partly because of the spotty knowledge of linguistic diversity in the continent and the scarcity of adequate sociolinguistic descriptions of the ways in which Africans manage their language repertoires. Despite these limitations, a sociolinguistics of intra-continental African migrations will contribute significantly to a better understanding of the conditions, nature, and periodicity of population contacts and interactional dynamics. It will help explain why geographic mobility entails reshaping sociocultural practices, including the language repertoires of both the migrants and the people they come in contact with. Moreover, the peculiarity of African economies, which rely heavily on informal non-institutionalized practices, prompts a rethinking of assumptions regarding the acquisition of the host country’s language(s) as the primary facilitator of the migrants’ socioeconomic inclusion. A sociolinguistic understanding of migrations within Africa can help to formulate new questions and enrich the complex pictures that the study of other parts of the world has already shaped.
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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 | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".