“Exploring the Green Frontier within Europe’s Recent Forest Initiatives”
Bibliographic record
Abstract
In recent decades, there has been a global consensus on the urgent need for coordinated efforts to combat forest loss and degradation, given forests’ critical roles in climate change mitigation, biodiversity, and local economies. Major policy initiatives, including the Bonn Challenge, the UN Decade on Ecosystem Restoration, and the European Green Deal, reflect this consensus. However, the development and implementation of forest policies are complex and politically charged, often addressing ’wicked’ problems with diverse actors and conflicting values. The proposed solutions—such as conservation, rewilding, certification, and forest expansion—introduce their own challenges. At the same time, there is growing concern about the commoditization and commercialization of forests, where green initiatives can exacerbate inequalities and facilitate new forms of resource accumulation. This paper introduces the concept of ’green frontiers’ as a lens to better understand patterns and consequences of this new forest dynamic in Europe. Applying critical perspectives typically used for frontier studies in the Global South to the Global North, this paper addresses a gap in literature on frontier-making in Europe while highlighting how environmental discourses are reshaping landscapes and communities, often reflecting historical patterns of dispossession and exploitation. It argues that anthropology and like-minded disciplines that rely on ethnographic and comparative methods, offer valuable perspectives for analyzing this formation of frontiers, and that a coordinated forest anthropology is particularly well suited to trace this shift within communities, as well as the common patterns across nations and regions.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".