A Journey Into a Researcher’s Positionality on Acculturation: Intersectional Identity and Immigration Milestones
Bibliographic record
Abstract
Exploring researcher’s positionality is prime in conducting research, especially in attempts to explore an experience of a phenomenon. This autoethnographic paper presents the positionality of an Arab Canadian immigrant researcher who has devoted her dissertation in social work to understand the acculturation process as a phenomenon experienced by Arab immigrant emerging adults in Canada, specifically in Windsor-Essex, southern Ontario. Herein forward, the article is written from the perspective of the researcher and is using first-person voice to best situate the journey of exploration through an autoethnographic account of acculturation as a lived experience. The researcher deconstructed her lived experience with the acculturation phenomenon, while commencing critical learning about Canada’s history of colonialism. This has altered her position from a resident to a settler in Canada. The article begins and ends with situating the self in the lens of immigration and refugee context, intersectional identity, as well as the experience, and how this exploration has landed the author in three milestones of her individual experience of the acculturation phenomenon. The article ends with contextualizing herself and sharing a current and future vision of exploring herself and transitioning from a resident to a settler.
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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 | 0.044 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.058 | 0.076 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".