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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".