Bibliographic record
Abstract
COIL@UArctic is a new University of the Arctic thematic network designed to promote and facilitate collaborative online international learning and biodiversity education. COIL (Collaborative Online International Learning) is a sustained educational approach where groups or individual students from one institution collaborate with groups or individual students from another institution, in a different country and/or culture, on sustained and assessed projects or assignments, developed collaboratively by tutors from each partner institution. The learning takes place online using freely available and commonly used communication technology. This type of cost-effective, experiential learning promotes intercultural competence, as well as the attitudes and reflective behavioural skills vital for a globalised economy. Students who undertake COIL projects use real-world scenarios to learn how to research global issues, set objectives, coordinate time zones and schedules, complete tasks, and navigate communication, language, and organisational challenges within and between international teams. The COIL@UArctic network is designed to promote unique educational opportunities and higher academic collaboration, enabling more people to harness and contribute to the growing body of knowledge, expertise, networks, and pedagogical advantages COIL offers students and faculty in the post-pandemic, technologically blended educational context. The network's focus on biodiversity education has the potential to provide knowledge for better lives and environments for all UArctic and non-UArctic members. Since October 2023, alongside faculty partners from Eastern Finland, Maine USA, Iceland, Canada, and Scotland; an Indigenous consultant from Alaska has been involved in the design of the thematic network to promote inclusivity in the development process and final deliverables which include an extensive set of web-based resources and training. Students from partner countries were also consulted about their views on COIL pedagogy and the COIL@UArctic network through an online focus group helping to inform the network development. This paper will share the key outcomes and reflections from the experience of seeking to adopt an inclusive approach to the development of a new international educational network.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".