Indigenous consultancy and collaborative online international learning: thematic network on collaborative online international learning and biodiversity education across the Arctic Circle (COIL@UArctic).
Bibliographic record
Abstract
COIL is part of the emerging field of Globally Networked Learning or Virtual Exchange, which involves educational initiatives using technology to facilitate cost-effective communication and collaboration across cultures. This type of 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 team objectives, coordinate different time zones and schedules, complete tasks using industry standards and globally-recognised social media platforms, overcome technological issues, negotiate differing expectations and deadlines, deal with varying degrees of engagement and reliability between teams, work remotely instead of face-to-face, and navigate communication, language and organisational challenges within and between international teams. COIL@UArctic is a new thematic network for collaborative online international learning and biodiversity education across the Arctic region. The network is designed to enable more people to harness and contribute to the growing body of knowledge, expertise, networks and pedagogical advantages COIL offers to faculty and students in the post-pandemic Higher Education context. Since October 2023, alongside partners from eastern Finland, Maine (USA), Iceland, Canada and Orkney (Scotland), an indigenous consultant from Alaska has been involved in the design and development of this thematic network to promote inclusivity in the development process and final deliverables. This paper will share key outcomes and reflections from this experience.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".