Riding the Waves of Flux: Newcomer Narratives on Their Lived Experiences Inside and Out of the Language Instruction for Newcomers to Canada (LINC) Program
Bibliographic record
Abstract
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".