Digital technologies & linguistically and culturally relevant pedagogies: where do we stand?
Bibliographic record
Abstract
This special issue highlights research mainly conducted from 2020 to 2023 in Canada, France, Germany, the UK and Finland. Each of these studies presents the affordances and constraints of using multilingual digital tools to address the deficit orientation to the education of newcomer students that still exists in many contexts. This includes the underestimation of the potential of multilingual students as well as the exclusive focus on using the language of the school for teaching and learning. It highlights the crucial role of teachers in supporting newcomer students and emphasises the innovative nature of using digital technology in STEM education. The six articles that make up this special issue focus on linguistically and culturally relevant online learning resources and curricula designed to support inclusive learning in STEM subjects. Focusing on teachers and their ideologies as well as teacher training, the articles highlight the varying degrees of effectiveness of multilingual technology in providing new ways of integrating newcomer student perspectives into curricula and promoting inclusive STEM education.
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.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".