Language use along the urban street in Senegal: perspectives from proprietors of commercial signs
Bibliographic record
Abstract
Senegal adopted French as the country's sole official language at the time of independence in 1960, since when the language has been used in administration and other formal domains. Similarly, French is employed throughout the formal education system as the language of instruction. Since the 1990s, however, government has mounted an ambitious adult literacy programme, in which Wolof, widely spoken as the lingua franca in multi-ethnic urban communities, together with other national languages are used as the media of instruction. Results from a study of language use in shop signs conducted in a suburban town near Dakar, the capital city, reflect these policies. Nearly half the shop proprietors had chosen to display signs entirely in French, some in the belief that the use of French was obligatory, others regarding French as the language the customers they wished to attract would best understand. Nevertheless it is evident that Wolof is also emerging as a written language. Nearly one-quarter of the proprietors employed Wolof in their signs, generally in combination with French. The results point to important issues which need to be addressed in the planning of language instruction both in the formal schools and in non-formal literacy programmes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".