Applying Local Language: Communication on the Road in a Multilingual Society
Bibliographic record
Abstract
The sociolinguistic phenomenon called multilingualism has created two different camps. The first camp is the camp of those who believe that the phenomenon is a curse to the society while the second camp believes that it is a source of novel delight and subtle experience, a blessing. Those who believed that multilingualism is a curse find their solace in the biblical history of the tower of Babel as recorded in Genesis 11:9, as a punishment for people’s pride. The history of the Dolgan fairy tale and the mother goddess of Acola tribe in New Mexico also supported this view. The Dolgan fairy tale claims that diversity of the tongue is a punishment of people’s quarrelling. The New Mexico mother goddess curses his people with multiple tongues to prevent quarrelling. However those who supported the view that multilingualism is a blessing also find their solace in the words of Holy Quran 30:22, where diversity of tongue is seen as a blessing and the new testaments account of the Bible in the Act of Apostle 2:4, where the apostles were empowered with the miraculous gift of tongue. This work touches upon the view of multilingualism as a blessing and therefore advocates the need to explore the use of mother tongue in multilingual society to give road instructions. This, we discover will reduce road mishaps in our society. Questionnaires were set out to ask about the educational qualification of most commercial drivers and to know which language they will prefer to see the road instructions. Most of them will be delighted to see the instructions in their local languages. Road users within Ibadan metropolis were given questionnaire to respond to. Three hundred questionnaires were administered while two hundred and eighty one were recovered which signifies 93.7%. The result shows that most of the road users prefer to see road instructions in their mother tongue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".