Special Issue Editorial Some Frontiers in Open and Distance Learning in the North
Bibliographic record
Abstract
The high North is a vast area with huge contrasts and strong seasonal variations.The situation in the North is special because the region for many years has been impressed by closed borders -a border that separated the East from the West and that represents great language, economic, technological, and social differences.However, the peoples in the high North have much in common, such as their closeness to nature and its seasons.Some years ago the concept of the digital divide was used to describe the East-West situation of the North.The digital divide refers to the gap between people or nations with effective access to digital and information technology and those with very limited access (Compaine, 2001).It includes the imbalance in physical access to technology and in the resources and skills needed to effectively participate as a digital user.The term was introduced in the 1990s to describe the gap in ownership of computers between ethnic groups, but later it was also utilized to refer to differences in access between countries.Education is one area where the digital divide makes an impact.And by bridging the digital divide, it is possible for regions to enhance communication with other countries and therefore to offer better educational and social opportunities.The University of the Arctic (UArctic) started in 2001 as a virtual university with the mission to "Empower the residents of the Circumpolar North, by building human capital through higher education."The realities of the Arctic region with its vast distances, great cultural diversity, and small communities and institutions called for a common effort by the involved nations and universities.UArctic is a cooperative network of universities, colleges, and other organizations committed to higher education and research in the North.UArctic has developed innovative courses offered in the classroom and in the field or delivered online, including north2north student exchange programs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".