Symposium 2: Virtual networks and the new definition of Knowledge
Bibliographic record
Abstract
The radical transformation of knowledge access and knowledge production for teaching and learning that higher education institutions around the world are currently experimenting due to the use of technologies and virtual networks, constitutes a platform from which to build new networked learning and new academic interactions. The possibility of instant access to multiple resources of information is transforming the way users around the world access, produce and learn knowledge. However, this significant transformation has not always been echoed by a formal policy agenda at the institutional level. There is still a big gap between technology applications in higher education, and the creation and application of policy strategies to enhance and to regulate these interactions. This paper explores the impact of the Internet in teaching and learning processes at the start of the 21st century and examines the opportunities to stimulate discussion towards policy creation. A discussion about Internet access and differences between Universities in Europe and Africa is introduced. The authors examine the connectivity between users, contents and technologies, and provide a platform for reflection from which to examine the role of the Internet and the creation of virtual environments as important participants of this transformation. Complex processes of knowledge access and knowledge transformation are explored in the framework of policy analysis within higher education institutions around the world.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".