Bibliographic record
Abstract
I argue that Model Arctic Council (MAC) has a role to play in Arctic sustainable development. Like the better-known Model United Nations (MUN), MAC is a form of simulation pedagogy, an experiential learning process in which secondary-school pupils or university students comprehend the nature and importance of complex issues such as sustainable development by imagining themselves as diplomats trying to negotiate a common approach to them. After demonstrating the educational value of diplomatic simulations in general, I introduce MUN as its most popular form, and I assess a case-study of a global MUN program designed to advance knowledge and action among youth in respect of the UN Sustainable Development Goals. This case-study, taken together with the structure, subject-matter and educational value of MAC itself, strongly suggests that MAC can be used to advance knowledge and action among both Arctic and non-Arctic youth in respect of Arctic sustainable development, including understanding how the notion of sustainable development is both contested in general and contextualized in the Arctic. Combining this analysis with professional experience, I offer practical recommendations to educators about the effective design and use of MAC and other simulation pedagogies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".