Mapping the interplay between open distance learning and internationalisation principles
Bibliographic record
Abstract
Open distance learning is viewed as a system of learning that blends student support, curriculum and instruction design, flexibility of learning provision, removal of barriers to access, credit of prior learning, and other academic activities such as programme delivery and assessment for the purpose of meeting the diverse needs of students. Internationalisation, on the other hand, is viewed as a process that blends intercultural international dimensions into different academic activities, such as teaching, learning, and research, into the purpose and functions of higher education. The common feature in the narratives that define open distance learning and internationalisation is the blending of university services to achieve specific outcomes. This blending feature has instigated an inquiry into identifying the interplay between the two concepts in as far as how the concepts are defined and what their goals and rationale are in the context of higher education institutions. While there are a breadth and variety of interpretations of the two concepts, there are differences and common features. The purpose of such an analysis is to open a new window through which institutions of higher learning can be viewed.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".