Learning to Belong: An Autoethnography on Acculturation and Identity Negotiations
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».