Frame Analysis of ENGO Conceptualization of Sustainable Forest Management: Environmental Justice and Neoliberalism at the Core of Sustainability
Bibliographic record
Abstract
Normative judgments on sustainability underpin concepts that shape the supply scenarios of timber consumption. The modern understanding of sustainable forest management is shaped by a diverse spectrum of social demands, going beyond the principle of sustainable yield management. Rival stakeholders compete to incorporate their ideas and interpretations of sustainable forest management into policy institutions. Environmental non-governmental organizations (ENGOs) have emerged as one of the dominant stakeholders in the forest-based sector. We set out to explore ENGO-specific conceptualizations of sustainable forest management and investigate differences in understanding among various ENGOs. By conducting a frame analysis of ENGO press releases, we identified two master frames: environmental justice and environmentalist frames. A difference in the emphasis placed on procedural and distributive justice as well as a different standpoint in the commons versus commodity debate emerged as the main divergences between the master frames. The results of our study demonstrate how the differences between the master frames underpin different conceptualizations of sustainable forest management. On the one hand, the ENGOs associated with the environmental justice master frame advocate for the broader implementation of community forest management based on power-sharing. On the other hand, the ENGOs associated with the environmentalist master frame promote a wide range of approaches associated with ecosystem management and social forestry paradigms. Moreover, the ENGOs associated with the environmentalist master frame challenge the concept of sustainable forest management as defined by the Helsinki and Montreal process by advocating for ecosystem management. The ENGOs associated with the environmental justice master frame reject the mainstream concept of sustainable forest management in any guise. Future research on ethical issues underlying forestry concepts may provide more conceptual and operational clarity for both forest managers and policy-makers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".