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Record W6959674903 · doi:10.11575/prism/46812

Riding the Waves of Flux: Newcomer Narratives on Their Lived Experiences Inside and Out of the Language Instruction for Newcomers to Canada (LINC) Program

2024· other· en· W6959674903 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeFocus groupPerceptionLived experienceVariety (cybernetics)Flexibility (engineering)Set (abstract data type)Qualitative research

Abstract

fetched live from OpenAlex

Abstract This research was aimed at studying the stories of nine former newcomers in the Language Instruction for Newcomers to Canada (LINC) program which is offered across Canada. The study of multiple individual stories is naturally qualitative in scope. It set out to explore nine individual stories and to learn from the perceptions of former newcomers previously in the LINC program and its effects/influences on their lives inside and out of language instruction classes. The study employed Narrative Inquiry to ensure a holistic picture of each individual’s life’s story was included as part of the data. The sources of data include (a) focus groups sessions, (b) semi-structured interviews, and (c) manual coding with reflective notes. The findings indicated several influences on the nine individuals of coming to Canada including the overall satisfaction with LINC, and its effect on their future pathways. In addition, this study’s newcomer-participants point to a variety of feedback that could be applied to future LINC participants’ learning and general programming. The various stakeholders, including policy makers, administrators, practitioners, and researchers, that work with LINC programs, may find the findings and recommendations particularly useful in extending and expanding on the flexibility that existing LINC programming already offers. Keywords: LINC, PBLA, multiculturalism, newcomers, narratives, flux

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.342
Teacher spread0.287 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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