Bibliographic record
Abstract
The Arctic is a vast, sparsely populated area. The demographic situation points to online distance education as a solution to support lifelong learning and to build competence in the region. An overall aim of all university education is what Hans Georg Gadamer calls Bildung, what we in Norwegian call dannelse and what Richard Rorty has called edification. A first problem to be addressed here is that in online distance learning some teachers find that is harder to support the development of the student’s voice. Being able to express oneself and to position oneself in a scientific community is vital for a well educated graduate. Another problem in online education has been the extensive use of writing as a means in the student’s learning process. Writing is vital to academic education, but in online courses there is in general a danger of overuse. At the University of Tromsø we have tested the web conference tool Elluminate Live. This is a real-time application, integrated in the University’s learning management system (LMS), Fronter. The application enables synchronous oral dialogue, simultaneous sharing of texts, and so forth. I present our main experience with the use of Elluminate Live and discuss the extent to which this application has turned out to be helpful in developing the quality of online courses.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".