Arctic sustainability, key methodologies and knowledge domains a synthesis of knowledge I
Bibliographic record
Abstract
"This book provides a first-ever synthesis of sustainability and sustainable development experiences in the Arctic. It presents state-of-the-art thinking about sustainability for the Arctic from a multi-disciplinary perspective. This book aims to create a comprehensive, integrative knowledge base for the assessment of Arctic sustainability for countries such as U.S., Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, alongside emerging ideas about sustainable development in the Arctic. These ideas relate to understanding how a community's geography matters in determining the required sustainability efforts, decolonial thinking for building sustainability that is crafted by and for local and Indigenous communities, and the idea of polycentrism, i.e. that the paths toward sustainability differ among places and communities. This volume also highlights the recent thinking about sustainability and resilience over the past decade for the rapidly changing Arctic region. With patterns of thinking drawn from economic, social, environmental, community and other components of sustainability, observations and monitoring, engagement of Indigenous knowledge, and integration with policy and decision making, the book helps us understand the complexity and interconnectedness of current Arctic transformations in a more comprehensive way"--
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".