Bibliographic record
Abstract
In this end piece, we argue that while this special issue shifts debates on the digital divide to address students' capacity to use Information and Communication Technologies (ICT) for productive social purposes, access to ICT remains a major challenge in countries like Uganda, in which less than 1% of the population has access to the Internet. However, since the case studies address marginalised communities in Australia, Brazil, Greece and South Africa, the findings have relevance to Uganda and other developing countries. Five lessons, in particular, are important for curriculum planning and policy development in Uganda: the need to collect empirical data on ICT access and use; the importance of recognising local differences across rural and urban communities, male and female students; the need to promote professional development of teachers so that they can make effective use of ICT in classrooms; the importance of integrating in and out-of-school digital literacy practices; and the need to consider how global software can best be adapted for local use. We conclude that if ICT is to play its part in achieving Education for All by 2015, there is an urgent need for collaborative partnerships between a wide range of stakeholders at both the local and global level.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".