Integrating cultural perspectives in pro-sustainable-forest-management behavior: Evidence from Aboriginal and non-Aboriginal groups in Ontario, Canada
Bibliographic record
Abstract
Incorporating stakeholders' forest values, a key to Sustainable Forest Management (SFM), requires an understanding of stakeholders' beliefs and values and their relationships to pro-SFM behavior. In a cross-cultural context, it is essential to understand cultural differences in these attributes and relationships, and use culture-sensitive data elicitation and interpretation methods. We proposed a pro-SFM behavior model, that integrates the key elements of selected models pro-environmental behavior proposed in the environmental psychology and resource economics literature, to examine the role of assigned forest values (AFVs) and beliefs in pro-SFM behavior. We tested the model in the context of Aboriginal and non-Aboriginal forest stakeholders in Ontario, Canada, using the data collected through surveys and field experiments in three Aboriginal and three non-Aboriginal communities. Our key findings are: (i) the rankings of different domains of AFVs are different among Aboriginal and non-Aboriginal groups; (ii) for Aboriginal people, their AFVs assigned from the community perspective have significantly higher mean scores than their AFVs assigned from their individual/household's perspective; (iii) the community AFVs and the individual/household AFVs were the better predictors of pro-SFM behaviors for Aboriginal and non-Aboriginal groups, respectively; and (iv) AFVs have significant mediation effects between environmental worldviews and pro-SFM behavior for non-Aboriginal people, while environmental worldviews directly influence pro-SFM behavior of Aboriginal people with no mediation through AFVs. The paper concludes with the implications of these results to SFM theories and practices and calls for incorporating cultural differences in designing SFM policies and practices. • Incorporating stakeholders' values is key to Sustainable Forest Management (SFM). • Reveal cultural variation in domain-specific assigned forest values (AFVs). • AFVs significantly mediate the effect of beliefs on pro-SFM behavior for non-Aboriginals. • Environmental worldviews directly affect pro-SFM behavior (no mediation) for Aboriginals. • SFM theories and practices should incorporate cultural variations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".