Welcome to the Smallside! A triangular approach to immersion at the English Kindergarten of Kokkola.
Bibliographic record
Abstract
Research on bilingualism and immersion has shown that there are advantages involved in learning languages through immersion. However, these are findings from studies focusing on the outcomes of immersion. This thesis aims at determining how the participants in immersion, the teachers, the children and the parents, view the immersion experience as a whole and the benefits of such immersion.\n\nImmersion schools have spread from Canada, where they were first founded, till Europe and Asia. This thesis focuses on the Smallside of the English Kindergarten of Kokkola (EKK), which is providing English language education to young children aged from 5 to 7. This study uses qualitative data. Such data are collected through interviews, surveys and ethnographic research from the teachers, children and parents involved in the EKK. The data is analysed with grounded theory.\n\nThe analysis shows how participants experience and value immersion, and where they perceive advantages of immersion, such as the children developing higher self-esteem and better intercultural skills.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".