Comparing chiShona loanwords of monolingual and bilingual speakers: An Optimality Theory analysis
Bibliographic record
Abstract
ChiShona is a southern Bantu language spoken mainly in Zimbabwe. In Guthrie (1948) chiShona is classified as an S.10 language, an area which includes other Bantu languages such as chiKalanga and chiNambya. ChiShona like any other language has expanded its lexical stock by borrowing, mainly from the English language. The two languages have different phonologies; English has a much more complex syllable structure than chiShona. There are three major differences: first, chiShona allows open syllables only while English allows closed syllables. Second, chiShona does not allow complex onsets while English can have as many as three consonants in the onset position. Third, chiShona does not permit complex syllable nuclei while English allows long vowels and diphthongs in its syllable structure. This article compares the realisation of loanwords in the speech of chiShona monolinguals and chiShona- English bilinguals. Our findings show that monolingual loanwords are completely assimilated to suit the chiShona phonological structures while in the speech of bilinguals some marked features of the English language such as complex onsets, the lateral approximant and postnasal voiceless obstruents are retained. Both monolinguals and bilinguals do not allow closed syllables and diphthongs which they repair through vowel epenthesis and spreading respectively. The article demonstrates that monolinguals and bilinguals have different constraint hierarchies. The former rank markedness constraints higher than faithfulness constraints while the reverse is true for the latter.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".