Leveraging Peer Support for Mature Immigrants Learning to Write in Informal Contexts
Bibliographic record
Abstract
For adult newcomers to countries such as Canada, learning language is more than an academic task. Language proficiency is their gateway to long-term economic and social stability, but limited access to resources contributes to systemic inequities which disproportionately place immigrants at socioeconomic disadvantages. Many new immigrants rely heavily on informal peer-networks to pursue avenues of success within an unfamiliar and inadequate system. To explore how we could leverage such a peer-based approach to meet their needs for feedback and support when learning to write in English, we deployed a peer-based writing app with 16 participants. Post-deployment focus groups and analysis of writing artifacts reveal that the design of writing support tools should present transparent feedback from both peers and automated sources, foster community through semi-structured discussions, incorporate guided review, and scaffold affective development. We discuss how incorporating these elements into the design of community learning platforms can address the language literacy needs of diverse immigrant learners and foster more positive experiences for newcomers as they negotiate their evolving identities.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".