Decolonizing language learning in digital environments through the voices of plurilingual learners in the Global South
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
Abstract Digital pedagogies of empowerment are needed to shift discourses on marginalization, facilitate additional language learning, and sustain multilingualism. Grounded in plurilingualism and decoloniality as theoretical frameworks, this transformative mixed methods study explored the affordances of PluriDigit, a plurilingual, decolonial, and digital approach to language learning. This study was conducted with thirty six language learners enrolled in online language courses in a multilingual program in São Paulo, Brazil. We explored whether learners’ plurilingual and pluricultural identities and competence would change over time and the potential emergent contributions of PluriDigit to learner empowerment. Results from inductive and deductive analyses of three types of data indicate a shift in learners’ mindset from monolingual to plurilingual and pluricultural identity and a significant increase in plurilingual and pluricultural competence scores over time. Moreover, results show that PluriDigit offered a critical lens to plurilingualism, facilitating decolonial learning, agency, and relationality, as well as the development of voice in the target language. We argue that PluriDigit is one possibility of digital decolonial pedagogy that can empower language learners in the Global South and beyond.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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