Code-Switching and Code-Mixing: Insights into Portuguese-Umbundu Speakers in Huambo (Angola)
Bibliographic record
Abstract
This empirical research focuses on the dynamics of language contact in the province of Huambo (Angola), in the interaction between Portuguese and Umbundu speakers.The study sought to address two issues which were investigated in the corpus: (i) How do Portuguese and Umbundu speakers in Huambo switch their respective codes?ii) What social circumstances determine how Code-switching (CS) and Code-mixing (CM) occur between these speakers?This work employed the focus group methodological approach in an effort to encourage natural interaction among the speakers.The conversations of the three groups were recorded, namely group 1 (aged 12 to 17), group 2 (aged 18 to 27) and group 3 (aged 28 onwards).The aim was to trace the sociolinguistic variables that forced speakers to frequently switch between different codes.The findings obtained from the study suggest that the speakers performed CS and CM in the three groups sampled.Interestingly, the study also discovered that the age factor influences how frequently CS and CM are used.Theoretically, this research is grounded in the Language Contact theory with the main focus on the work of Inverno (2006Inverno ( , 2011););Figueiredo and Oliveira (2013); Oliveira (2014) and Formal Grammar for Code-switching (see Sankoff and Poplack, 1981).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".