The politics of sustainability in the Arctic reconfiguring identity, space, and time
Bibliographic record
Abstract
"The Politics of Sustainability in the Arctic argues that sustainability is a political concept because it defines and shapes competing visions of the future. In current Arctic affairs, prominent stakeholders agree that development needs to be sustainable, but there is no agreement over what it is that needs to be sustained. In original conservationist discourse, the environment was the sole referent object of sustainability, however, as sustainability discourses expand, the concept is linked to an increasing number of referent objects, such as, society, economy, culture and identity. This book sets out a theoretical framework for understanding and analysing sustainability as a political concept, and provides a comprehensive empirical investigation of Arctic sustainability discourses. Presenting a range of case studies from Greenland, Norway, Canada, Russia, Iceland and Alaska, the essays in this volume analyse the concept of sustainability and how actors are employing and contesting this concept in specific regions within the Arctic. In doing so, the book demonstrates how sustainability is being given new meanings in the postcolonial Arctic and what the political implications are for postcoloniality, nature, and development more broadly. Beyond those interested in the Arctic, this book will also be of great value to students and scholars of sustainability, sustainable development, and identity and environmental politics"--
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".