How can emerging technologies advance the creation of language-friendly and literacy-friendly schools?
Bibliographic record
Abstract
The evolution of digital technologies has frequently been hailed as a ‘game-changer’ in education. However, like previous technological innovations, such as television, these recent developments have failed thus far to demonstrate any significant large-scale improvement in the quality of educational provision or in educational outcomes. The papers in this special issue suggest that there is potential to change this scenario. Digital platforms such as Binogi have been able to exploit technological advances such as vastly improved crosslinguistic machine translation ushered in by artificial intelligence to make curriculum content much more accessible to multilingual students. Drawing on the papers in this special issue, I highlight three dimensions of digital learning environments that have demonstrated pedagogical credibility to enhance multilingual learners’ development of literacy and their acquisition of academic content in the target language: (a) they provide extensive access to and promote engagement with written (and oral) input in the target language, (b) they provide instructional scaffolds within the digital environment to promote both awareness of how language works and intentional learning of academic concepts and subject matter content, and (c) they encourage and enable students to become autonomous learners who are capable of self-regulating and evaluating their own learning.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".