Bibliographic record
Abstract
The Arctic encompasses a myriad of ecosystems, transcending borders between nations and cultures. It is home to 4 million people, including numerous Indigenous groups making up around 10% of the population. Although underexplored, it is also host to a variety of geologically diverse mineral deposits that are critical to the production of renewable energy and our ability to achieve our climate goals. However, the impact of climate change on the Arctic is magnified and some of our solutions to climate change have the potential to have negative local impacts. Furthermore, the history of mining in the Arctic raises understandable concerns as to whether or not we should be exploring and mining in the Arctic. This article discusses the interplay between the environment, people and development in the Arctic, with a specific focus on the history of exploration and mining in the region. We pose questions such as: “How do we balance the global need for minerals with environmental and social concerns around resource extraction?”, and “can we envisage a future for mining in the Arctic which ensures long-term sustainability, environmental stewardship and Indigenous wellbeing and collaboration?” The answer to some of these questions might lie in examples of more successful resource development in the Arctic, which include Indigenous benefit agreements, braided knowledge systems and shared ownership projects. It is clear that only by incorporating a diversity of voices and partnerships, and challenging business as usual in the Arctic, can we begin to conceive of potential solutions for achieving a just transition.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".