About landscape perception and the ability to communicate. Can landscape perception research provide a tool for starting a dialogue between different users of the countryside?
Bibliographic record
Abstract
About landscape perception and the ability to communicate. Can landscape perception research provide a tool for starting a dialogue between diff erent users of the countryside? More and more, societys expectations of rural landscapes are rising and farmers are increasingly stimulated to incorporate green services int o their operational management. This assumes matching perceptions of the landscape between farmers, landscape experts and the general public. A psychophysical method based on a picture enquiry was used to measure landscape perception in the Pajottenland, a central Belgian, r ural area. Additional questions assessed the importance of meanings and functions of the landscape and revealed differences in perception among three target groups (farmers, landscape experts and country-dwellers). T he results confirmed that the three groups look at landscapes in a diffe rent way, attaching importance to different landscape features and findi ng different functions appropriate for the considered landscapes. As a c onsequence, policies concerning landscape servicing by the farming commu nity should incorporate appropriate incentives of communication and gene rate modes of understanding between different stakeholders. Rogge, E. Nevens, F. and Gulinck H. 2007. Perception of rural landscapes in Flanders: Looking beyond aesthetics. Landscape and Urban Planning, Volume 82 (4) pp. 159-174 The intensification of greenhouse horticulture is a commonly occurring t rend in many regions around the world, including the Netherlands, Austra lia, Canada, Spain, the U.S., and the U.K. One typical characteristic of this intensification is that high-technology and large-scale greenhouse s are being built. An additional phenomenon is the clustering of several of these large greenhouses on a single site, into so-called greenhouse parks. The main incentive for this clustering is the reduction of prod uction costs by sharing infrastructure such as energy, water and gas fac ilities. In Flanders the development of greenhouse clusters is being enc ouraged and promoted by the Flemish governments Agricultural Department . One of the major problems all developments of this size face is their impact on the aesthetics of the surrounding landscape, and this may even prevent their realisation. In recent years there has been an increasing resistance against the construction of large greenhouses in Flanders. T he visual impact of these clusters seems to be one of the major obstacle s to their public acceptance. The wider publics perception of large gre enhouses should therefore not be neglected by policy makers and planners if they want to succeed in developing large-scale projects. This percep tion is, however, hard to objectify, let alone to measure. In this paper , we demonstrate the value of a GIS-based method to objectively quantify the visual impact of large-scale greenhouse developments. We also asses s the potential of a GIS-based planning instrument to evaluate the effec tiveness of landscape design plans. Rogge, E., F. Nevens and H. Gulinck (2008) Reducing the visual impact of greenhouse parks in rural landscapes. Landscape and urban planning 87 (1) pp. 76-83 The intensification of greenhouse horticulture is a notable trend in man y regions around the world. This intensification causes the grouping of large-scale greenhouses on a single site, into so-called greenhouse clu sters. The main incentive for clustering is the reduction of production costs by sharing infrastructure such as energy, water and gas facilitie s. Despite these advantages, the public remains sceptic towards greenhou se clusters and resistance in Flanders is frequent and often fierce. The objective of this research is to obtain insight into the reasons, under lying motives and processes that steer this resistance. A grounded theor y approach resulted in a comprehensive theoretical scheme that visualize s the key factors that underlie and make up the process of public resist ance. In addition to the expected NIMBY-syndrome and the fear for the lo ss of landscape quality, societal values, market related factors and str uctural problems also play a part in the formation of the public attitud e towards greenhouse clusters. Rogge, E., Dessein, J. and Gulinck H. 2008. Public attitude towards majo r landscape changes: The case of greenhouse clusters in Flanders. Submit ted to Sociologia Ruralis
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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 | 0.002 | 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 | 0.001 | 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".