Academic Writer Identity Construction and Awareness in the Canadian Context: The Experience of Undergraduate Multilingual Student Writers
Bibliographic record
Abstract
Many studies have focused on academic writer identity and how it manifests in English writing as an additional language (LX) (Lehman, 2018; Zhao, 2019). However, few studies on multilingual student writers (MSWs) writing in English as their LX have examined students’ awareness of their academic writer identities and the contribution of such awareness to their academic texts. To bridge this gap, I investigated the experiences of undergraduate MSWs in a Canadian academic context. Specifically, I explored the academic writer identity construction of these learners, the extent of their awareness of these identities, and how awareness of such identities contributes to their actual writing. I also examined the extent to which reader-instructors construct learners’ academic writer identities, the (non-)linguistic features of the texts that support readers’ inferences about students’ academic writer identities, and whether there are discrepancies or similarities between the academic writer identities that student participants intended to project in their text and the identities that readers constructed. The study’s theoretical framework encompassed a social constructivist view of identity (Ivanič, 1998), critical language awareness (Fairclough, 2013), translanguaging theory (Garcia, 2009), and systemic functional linguistics (Halliday, 1994). Nine undergraduate students and three instructors participated in this research. Semi-structured interviews, final papers, and an-open-ended questionnaire were used to collect data. Data analysis followed Creswell’s (2015) six step process for interpreting qualitative data. Findings suggest that (a) MSWs’ respective academic writer identities are (re)constructed and influenced by various elements, such as privileging power and instructors’ biases in their academic setting; (b) their academic writer identity awareness is associated with their (non-)linguistic choices, and such awareness affects their self-representation in their texts; (c) readers draw on various (non-)linguistic features to construct the academic writer identities of the writers, and (d) many factors can cause discrepancies between writers’ intended identities and the identities inferred by readers, such as the extent of writer’s awareness of influential factors on self-representation in the text, challenging writing conventions, and cultural differences between reader and writer. The study concludes by discussing the implications of the results and proposing recommendations for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".