Bibliographic record
Abstract
This book celebrates the University of the Arctic Thematic Network on Teacher Education for Social Justice and Diversity. The Network was established in Ulan Ude, Republic of Buryatia in Russia, in 2015 with six founding member organisations from Finland, Canada, Russia, Scotland and Mongolia. Led by the University of Lapland, the Network is finding its feet and gradually establishing itself. In three years, it has grown quickly and currently hosts 22 organisations that share interest in promoting social justice and resilient societies through teacher education. The Network now includes institutions from all of the eight Arctic countries as well as Scotland, Mongolia and France. \nThe Network is in line with the Finnish Chairmanship of the Arctic Council’s priority area of education. From 2017 to 2019, the Arctic Council’s Sustainable Development Working Group hosted the project ‘Teacher Education for Diversity and Equality in the Arctic’, which emphasises that teachers are the key factor in providing a quality education. To promote sustainable communities, teachers who work in the Arctic and in northern communities must be committed to their work and be inspired by the Arctic. The project has strengthened the Network of education specialists in the Arctic in cooperation with the University of the Arctic. This book is part of that project’s outcome and an excellent example of global networking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.203 | 0.116 |
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".