Complex language encounters: Observations from linguistically diverse South African classrooms
Bibliographic record
Abstract
This article reports on the initial observation phase of a larger, longitudinal project that explores complex language encounters in grades R (Reception) to 3 classrooms in South Africa. Complex language encounters refer to teacher-learner exchanges that take place when neither teachers nor learners are first language speakers of the language of instruction, in this case English. Observations during teaching practice visits to linguistically and culturally diverse South African urban classrooms yielded several vignettes that illustrate the need for teachers to be provided with strategies to lessen the confusion of some language encounters. Although preliminary, our findings underline how critical it is for teachers to possess full proficiency in the language of instruction as well as cross-cultural competence. That is, in order to attend adequately to diverse learners’ sense-making efforts, teachers need to know how to relate to learners by ‘border crossing’ linguistically, culturally and conceptually.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".