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Record W2216960828 · doi:10.25316/ir-153

From Korea to Cowichan: a Korean perspective on English language learning in Duncan

2011· dissertation· en· W2216960828 on OpenAlexaboutno aff
Adam Reid

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2011
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)LinguisticsPsychologyComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The purpose of the present study was to gain a better understanding of what opportunities for successful learning and socialization that young South Korean ELLs encountered at X Elementary School in the Cowichan Valley on Vancouver Island in British Columbia, BC. In order to achieve this aim, the author of the current study set out to determine the students’ perceptions of various factors that could influence their immersion experience in Canada. The questionnaires created for this study were gathered from seven South Korean ELLs in Grades 4-7, all of whom were residing in the Cowichan Valley and attending X Elementary School at the time of the study. The study found that the young South Korean ELLs enjoyed being at X Elementary School and felt confident about about their English abilities, but were unhappy about studying certain subjects at school as well as being displeased by certain dynamics within the Canadian homestay placement. The data from the survey showed that areas of social dynamics and food considerations were of vital importance to the happiness and success of the immersion experience for young South Korean ELLs at X Elementary School.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.293
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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