Journal of Arctic Climate Security Studies, Vol. 1, No. 1. Ted Stevens Center for Arctic Security Studies, U.S. Department of Defence
Bibliographic record
Abstract
This review introduces the U.S. Department of Defense's (DOD) Ted Stevens Center of Arctic Security Studies (TSC), one of six regional centers for security studies.It also reviews the first issue of the TSC's Journal of Arctic Climate and Security Studies (2023).The center is part of a change in U.S. security policy that focuses on homeland defense that now includes Alaska.Instead of using Alaskan-based troops to fight in the Middle East, as it did during the past 20 years, DOD is reorienting armed forces stationed in the Arctic to bolster domestic security and to meet the United States' NATO Article 5 commitment.Melissa Dalton, former assistant secretary of defense with center oversight, outlines the TSC's three main tasks in her journal essay.These tasks are to provide executive education for DOD senior leaders; to foster outreach and engagement for Alaska military organizations, Native Peoples, and allies; and to conduct high-quality research and analysis to improve DOD's Arctic knowledge base.The TSC established short seminars and five-day courses on the Arctic for senior civilian and military leaders, which are held both remotely and at various Alaska locations.Center staff facilitate outreach and engagement by interacting with Alaskan Indigenous communities and hosting tribal leaders at meetings and ceremonies.TSC associate director Craig Fleener is an accomplished Indigenous leader involved with the Arctic Council Permanent Participants and a senior Alaska Army National Guard officer.TSC leaders such as retired Coast Guard Admiral Matthew Bell, the center's dean, attend international forums such as the annual Arctic Circle Assembly in Reykjavik.Thus, the center seems to be meeting the first two tasks adequately.This review will focus on the last task-conducting high-level Arctic research and analysis-by looking at the scholarly quality of the TSC's first journal issue and the capability of the center's staff to engage in its own original scholarly research.Fittingly, the journal begins with well-wishes by the Ted Stevens family, followed by Alaskan Senator Lisa Murkowski's introduction.As assistant secretary of the Interior, Ted Stevens advanced Alaskan statehood,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".