Social values and the contingent nature of public opinion and attitudes about forests
Bibliographic record
Abstract
In recent years there has been considerable emphasis placed upon the importance of understanding "forest values." This article has two interrelated objectives: 1) to provide a discussion of some of the methodological issues to be considered in conducting social survey research on forest values, opinions, and attitudes; 2) to provide an analysis that illustrates how values underlie opinions and attitudes about clearcutting and visual quality. In the first part of this paper a critical overview of using social science to investigate forest values is undertaken. For illustrative purposes, aesthetic values, clearcutting, and visual aspects of forest management are focussed upon. The concept of "values" is critically examined. The importance of understanding the connection between values and opinions/attitudes is discussed. Some problems with defining "the public" are described, and the need to explore intergroup differences with regard to environmental issues is stressed. In part two of the paper an analysis of empirical data is provided to demonstrate: 1) the relationship between abstract forest values (e.g., aesthetic values) and opinions about more specific forest practices (e.g., clearcutting, the creation of visual corridors); 2) the ways in which abstract forest values are associated with world views that structure opinions and attitudes about forest management (e.g., ecological, outdoor recreation experiences, and aesthetic values are part of a non-material forest values world view, while economic values are part of an economic values and community sustainability world view). It does this by providing a series of bivariate and multivariate analyses looking at the effects of abstract forest values, and world views upon opinions/attitudes about visual forest management (while controlling for other factors—such as socio-economic and demographic variables). Implications of these results for future research, and for forest managers, planners, and policy makers is also briefly discussed. Key words: values, attitudes, opinions, world views, sociology, survey methods
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".