Toward Transformative Inclusivity through Learner-driven and Instructor-facilitated Writing Support: An Innovative Approach to Empowering English Language Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it