The Effects of Educational Tools in Reducing Code-Switching in Child Simultaneous Bilingual Education
Bibliographic record
Abstract
Simultaneous bilingual education of a child is a dynamic process. Construction of linguistic competences undeniably depends on the conditions of the linguistic environment of the child. This education in a monolingual family, requires the practice of parenting tactics to increase the frequency of the language use in minority, during which, code-switching prevents child from keeping the monolingual rhythm in the minority language. This case-study focuses on a 41 month-old girl whose only interactive source for the second language, i.e., French, is her non-native mother, since birth; Persian is the dominant social language. Seeking to promote language acquisition by offering several opportunities for the weaker language, “animation”, accessible in every house, is introduced as an audio-visual educational tool. This paper experiments the application of a parental method to see if this passive tool can be used to create interaction and communication, how effective can such a document be on child language development while limiting code-switching and minimal level of expression and thus analyzing language learning of a child being exposed to two languages in a monolingual social environment. This research aims to prove the effectiveness of cartoon as an educational tool in improving the quality of a minority language acquisition by designing age-adapted activities that have been tested earlier to educational goals by the mother-researcher on primary school children. All sessions of this experiment were subjected to an audio recording which allows meticulous observation and data evaluation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".