Adapting Education to a Changing Arctic Climate With School-Community Partnerships
Bibliographic record
Abstract
Students experience more meaningful opportunities and greater understanding by participating in studies and learning in one’s own community. This paper outlines a proposed doctoral research project, that evolved out of previous research on how the Nunavut curriculum could respond to climate change in formal K-12 schooling based on hybrid epistemologies of Inuit Qaujimajatuqangit (Inuit knowledge) and Western science, on adapting education to a changing arctic climate through school-community partnerships. The research will take place in the Arctic community of Iqaluit, Nunavut and aim to develop a pedagogy that evolves with the changing climate and is rooted in Inuit Qaujimajatuqangit ; Inuit Societal Values, beliefs and attitudes; critical pedagogy of place; and Western science. The initial stage of research will examine current educational responses, school-community partnerships, and challenges. The second stage will involve working closely with a group of representatives to develop a school-community partnership model that assists educators to respond more reflexively to a changing arctic climate. This proposed research provides a unique contribution to the field, as it will examine how educators in Iqaluit are responding to climate change, current partnerships, and how school-community partnerships can work to create education models that suit a specific cultural and geographical context.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".