The Potential of DPSIR Framework to Develop a Holistic Picture of Arctic Industries and Livelihood—A Scoping Review
Bibliographic record
Abstract
The Arctic and its resources are becoming a hotspot of increasing political, environmental, and social conflict. The Driver-Pressure-State-Impact-Response (DPSIR) framework can be a useful tool when trying to disentangle the complex issues affecting the region and organize their fundamental components along a causal chain, thus promoting a much-needed integration between social and environmental sciences on one hand and science and policy making on the other (especially when a participatory approach is pursued). The aim of this article is to facilitate and improve future applications of the DPSIR framework in the Arctic context. This is pursued through a comprehensive literature review of the use of the DPSIR framework in the Arctic, with a focus on five of the most important economic sectors in the Arctic economy: aquaculture and fisheries, mining, forestry, tourism, and Indigenous livelihoods. In order to promote the most accurate and balanced approach to the DPSIR framework, its main criticisms and variants are also discussed. The article provides a summary of indicators used in Arctic case studies and focuses on the relevance of the framework as a tool for both local stakeholder involvement and participative policy-making processes. It also provides a general model for application of the DPSIR framework in the Arctic context and, when Arctic examples are not available, a summary of relevant examples outside the Arctic area.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".