Thriving through Uncertainties: The Agency and Resourcefulness of First-Year Chinese English as an Additional Language Writers in a Canadian University
Bibliographic record
Abstract
Amidst the increased enrolment of international Chinese English as an additional language (EAL) students in North American universities, scholars have reported on their academic and social experiences in navigating English-medium studies (e.g., Liu, 2016; Zhang & Zhou, 2010). Although informative, some studies focus on EAL learners’ perceived deficient language proficiencies, and how these deficits can negatively impact their academic success. In contrast to studies based on deficit models, this study argues that participants exhibit agency as evidenced in their responses to challenges encountered in and changes to their perceptions of and practices in academic writing. Employing an ecological perspective and (second) language socialization theories (Duff, 2010, 2019; van Lier, 2004, 2008), this qualitative case study examined how six first-year Chinese EAL learners enacted their agency and resourcefulness when navigating their academic writing trajectories. Ultimately, this study’s findings recommend that composition faculty, administrators, and EAL educators recognize EAL writers’ agency in accessing multiple resources while acknowledging their writing challenges, providing an optimal learning environment, and empowering them to thrive in their mainstream composition studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".