Exploring Plurilingual Pedagogies across the College Curriculum
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
Many students in US community colleges often speak a language other than English (LOTE) at home. They find it difficult to complete college requirements, and many drop out. Struggling with the acquisition of academic English and the content of their courses, they exhibit low academic confidence and are easily frustrated. In an attempt to raise students’ self-esteem and motivate them to remain enrolled, we explore plurilingual pedagogies across the college curriculum, in science, humanities, education, and linguistics courses. The four case studies presented demonstrate how we integrate dynamic translingual teaching practices such as translation, code-switching, cross-linguistic analysis, and the use of students’ linguistic repertoires to complete assignments in multilingual classrooms. We have found that plurilingual pedagogies enable students to discover their linguistic strengths and utilize them to complete college assignments. As bilingual faculty we found our educational goals supported and validated through interdisciplinary collaboration.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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