Toward Transformative Inclusivity through Learner-driven and Instructor-facilitated Writing Support: An Innovative Approach to Empowering English Language Learners
Bibliographic record
Abstract
English Language Learners (ELLs) have long been targets for linguicism (i.e., linguistic racism) as they are often subjected to judgement based on deficit models of language proficiency. To support ELLs during the COVID-19 pandemic, a long-running, co-curricular writing support program based on a Learner-Driven, Instructor-Facilitated (LeD-InF) approach was modified for fully online participation. Through this approach, ELLs develop academic reading, writing, and critical thinking skills, using their respective course materials and personalized responses from their writing instructors who provide inclusive learning opportunities that specifically address ELLs’ unique individual needs. This innovative anti-deficit, proactive, and risk-free approach not only increased learners’ willingness to write and volume of written output in their academic journal entries (objectively tracked through word count), but also developed learner identity, agency, autonomy, as well as confidence. Analysis of written output volume combined with learners’ end-of-program reflections provide pedagogical insights for addressing and redressing deficit models as well as combating linguicism, contributing important steps toward ensuring equity, justice, and transformative inclusivity so that diverse voices can be heard in the teaching and learning space.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".