The Adolescent from Poland: an Autoethnographic Journey of an Immigrant Experience in Canada
Bibliographic record
Abstract
This thesis explores the experience of a non-English speaking, immigrant adolescent from Poland to Canada. This is a self-study, I the subject and researcher at once. I have chronicled and traced the experience of my own immigration process using the qualitative methodology of autoethnography. The exploration of personal journals, yearbook entries, and reflective analysis have led me on a journey to delve into the loss, isolation, and identity shifts which I experienced as an adolescent. Through the personal experience of the author, this type of research can bring the reader closer to the subculture being studied. While every adolescent who immigrates has their own unique experience, the introspection and evaluation provided by this autoethnography can greatly facilitate an understanding of the process of transition. The experiences and struggles I faced, and the interpretations derived from them, can provide insight for counsellors who work with this subculture and strengthen my own practice as a counsellor. The results of this study were expressed in a personal narrative in Chapter IV. Chapters I through III present respectively an introduction, a review of literature, and the research methodology. Chapter V offers discussion of the findings, recommendations for counsellors working with immigrant adolescents and concluding remarks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".