There's more to landscape than meets the eye: towards inclusive landscape assessment in resource and environmental management
Bibliographic record
Abstract
Consideration of aesthetic values in resource and environmental management in North America emerged in the 1960s. It soon became enshrined as ‘visual resource management’, which emphasized a singular visual notion of aesthetics and an expert‐based approach to assessment. This paper challenges this dominant view. An empirical research study is presented in which a broader conceptualization of the landscape aesthetic and a participatory methodology for assessment were developed and used to explore the landscape experiences of inhabitants of the Cariboo region of British Columbia. Themes, categories and ideas of landscape experience grounded in participant perspectives revealed a richness of landscape and can be seen as an opportunity to supplement and enrich current landscape assessment and ‘visual’ management. More importantly, this conceptual and methodological reorientation in understanding landscape aesthetic sensibilities both reflects and supports the shift in current thinking within resource and environmental management more generally, from technocratic, state‐centred, expert‐based approaches to locally responsive (place‐based), participatory and inclusive approaches for dealing with environmental concerns and resource‐development issues .
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".