Bibliographic record
Abstract
Motivated by the Language Instruction for Newcomers to Canada (LINC) program’s autobiography component, which asked newcomer learners to share their life stories, this study investigates the impact of practicing autobiographical writing on adult newcomer learners’ English learning and their identities and self-reflections. Using a multi-staged action research design, this study involved a six-week autobiographical writing class I taught to eight volunteer learners, using lesson plans informed by interviews I had conducted with experienced English language instructors. This study includes the following data: instructor participant interviews (n=8), lesson plans and revision documents, learner pre- (n=8) and post-course interviews (n=4), learners’ written work produced in the course, and my reflections as the researcher, designer of the curriculum, and instructor of the course. Qualitative analysis of the data and the case studies of the focal learners reveal that autobiographical writing can support adult newcomers’ English learning and positively influence their identities and self-reflections. The findings in this dissertation call into question the LINC program’s decision to eliminate autobiography as a required component since it can help amplify learner voices and support their self-expression. As such, this study advocates for complementary research to engage more participants and various stakeholders to further examine the impact of autobiographical writing on language learners.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".