Learning to Belong: An Autoethnography on Acculturation and Identity Negotiations
Bibliographic record
Abstract
ABSTRACT The purpose of this research was to uncover my lived experiences that spanned two continents and four countries involving acculturation and subsequent re-acculturation. I have employed autoethnography to shed light on my experiences as an English Language Learner (ELL) in various linguistic, cultural and social environments while living, studying and working in Canada and the United States. I relied on rich narratives (Richardson, 2000) in the form of personal vignettes followed by intricate analysis and interpretation (Chang, 2008) to provide a layered account of my participation in different communities of practice (Wenger, 1998). The Methodology section presents an in depth portrayal of my deep reflexivity and constant dialoguing with my personal journal entries, vignettes, and additional data in the form of memoirs, autobiographies, and various life writings, as well as personal documents and artifacts that helped data triangulation. I also explored and included empirical literature that focused on autoethnography as a form of inquiry enabling me to connect and resonate with readers through various narrative expressions (Chang, 2008). The three main themes that emerged through illuminating my lived experiences in varied linguistic, cultural and social settings focus on my journey of learning to belong; Belonging through learning and academic achievements, Belonging through professional and educational constructs, and Belonging through multisensorial experiences. I employed Coelho’s (2016) acculturation framework to better understand my personal growth and Wenger’s (1998) social theory of learning to uncover my multiple social identities reflecting my diverse linguistic and cultural background. As a result, I could trace my steps of acculturation and uncovered the four tenets of my theory of belonging; Embracing my surroundings, Seeking out mentors, Active participation in all communities of practice, and Coming to terms with my multiple social identities. This study aims to resonate with audiences of adult ELLs who might uncover their strengths to best move forward and to achieve their dreams that help them feel academically grounded and as contributing members in various communities of practice. However, the circle of readers might be extended to both linguistically and culturally diverse educators as well as monolingual teachers who wish to become mentors and role models to those minority students who need to encounter strong individuals believing in them, so they can dare to hope and dream just like their classmates who are firmly grounded in their linguistic and cultural knowledge.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".