Arctic Infrastructure: Considerations in the Green Transition:Position paper written by scholars from Fulbright Arctic Initiative III
Bibliographic record
Abstract
The global green transition has put a new focus on the Arctic region and its resources (eg. energy, minerals, and access to land) at the same time as Arctic communities are looking for development, self-determination, and growth. Arctic infrastructural “fingerprints” will exemplify key considerations within the green transition in a changing arctic climate, with competing visions and framings of what the green transition is about, and the rationale for its need. Global green transition involves resources that may be found in the Arctic. The argument of this paper is built around the position that it is of particular importance to hear, value, integrate, and prioritizes the voices of Arctic Indigenous Peoples and others living in the North. Findings from fieldwork and observations conclude that: 1. The Arctic has a new strategic role because of the green transition, 2. Arctic communities lack physical as well as policy infrastructure for a successful transition, 3. Green transition is not “a one size fits all” in the Arctic. Different communities have different opportunities as well as requirements when it comes to green transition, 4. There is a knowledge gap both in terms of what arctic communities need from a transition and how these needs best could be met, and 5. Green transition can become an important driver of change in the Arctic.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".