Teaching Language, Promoting Social Justice
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
The article is concerned with teaching language by utilizing social media from a social justice perspective. It makes an argument for taking a dialogic approach to pedagogy based on serendipity and contingent scaffolding. The article is inspired by a small but growing body of literature known as Critical Computer-Assisted Language Learning. First, I provide a brief introduction to Computer-Assisted Language Learning, and its recent turn toward a critical approach. Then, I discuss social media, and what “social” means when it precedes the word “media.” Next, I describe how social media are being used in language education, and why the dominant methods of use may not prepare language learners as justice-oriented democratic citizens. A key barrier I identify in this regard is media users’ increasing ability to filter what they want to see and hear. To re-think the pedagogical uses of social media, I draw from Mikhail Bakhtin’s works and propose a dialogic approach, which may be helpful for language teachers and teacher educators.
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 | 0.000 |
| 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