Extractivist valorization in industrial forestry in the Global North – Elements of an analytical framework and illustration for the cases of Finland and Alberta, Canada
Bibliographic record
Abstract
This paper contributes to the political economic analysis of industrial forestry in the Global North (GN) by introducing and applying elements of an analytical framework for extractivist valorization. The proposed framework serves as a complement, systematization, and extension of the concepts of valorization and (post-fossil) extractivism. It scrutinizes the political-economic constellation and social as well as ecological sustainability challenges of current dominant practices in industrial forestry in the GN. The (potential) contribution and role of industrial forestry in social-ecological transformation processes is contested, although forestry is often perceived as a sustainable sector per se, and its services and products are crucial for many sustainability, bioeconomy, and decarbonization strategies. With the proposed analytical framework, the paper investigates forestry as an industry that socially and economically mediates relationships between individuals, society, and nature. The paper illustrates the analytical potential of the proposed framework by applying it to two exemplary cases of industrial forestry: Finland and the Canadian province of Alberta. The paper concludes that such a framework can provide relevant insights into the sustainability challenges in industrial forestry in both cases examined. New pathways of valuing and using forests need to be actively pursued to integrate the forest sector into the broader project of social-ecological transformations. • Constructs elements of an analytical framework for extractivist valorization. • Combining concepts of valorization and post-fossil extractivism. • Analyzes sustainability challenges in industrial forestry in the Global North. • Illustrative application of proposed framework to cases of Finland and Alberta. • Transformation towards sufficient, just, and caring forestry as future vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".