Bibliographic record
Abstract
Openness has been a feature of higher education for many decades, particularly through the establishment of open universities, although there remain debates about what openness means in practice. Digital technologies, some based on open principles, and digital content, aided by open licences, have both contributed recently to an extension of what is deemed possible under the heading of openness. Nevertheless, while in principle there may be greater degrees of openness available in higher education it does not mean in practice that many people can still readily avail themselves of these new opportunities to learn, not just because they do not have access to digital technologies but personal circumstances mean they also lack the necessary skills and the confidence to use such technologies in general or for education in particular. In fact it can be argued that this new openness, characterised mainly through the open educational resources movement, may actually widen rather than bridge the digital and educational divides between groups, both within and across national boundaries, through the increasing sophistication in technologies and the competencies expected of learners. This paper reviews some of the evidence supporting these different areas of interest and attempts to provide a synthesis of them. It then argues that actions may be required by many inter-mediaries to help to reduce the diverse social and cultural digital divides within education, including through the mediated use of open educational resources between teachers and learners.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".