Exploring the Theoretical Interface of Climate Change and Resource Dependency: Application to the Vulnerability of Boreal Forest Regions
Bibliographic record
Abstract
This paper addresses the combined effects of two sources of disturbance on the boreal forest – climate change and the economic relations of industrial forestry. It describes a theoretical blueprint constructed of concepts from the theory of dissipative structures (derived from the discipline of physical chemistry) and world-systems theory (derived from the discipline of sociology) into a proposed integrated theory pivoting on the concept of social vulnerability. The goal is to examine the key concepts of this theory – vulnerability, resilience and adaptive capacity – as elements of the complex systems perspective provided by dissipative structure principles. The focus on social vulnerability provides the means to establish the role of external economic linkages relevant to industrial forestry – the core/periphery relations of the world-system – as they influence the social vulnerability of the boreal forest SESs. These systems are posited as embedded peripheries, following world-system criteria, and as the focal scale of analysis within a larger hierarchically organized dissipative structure. The goal is to suggest and stimulate ideas for further discussion and exploration, motivated by the premise that any successful climate change mitigation efforts depend on having sound theoretical foundations on which to stand.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".