Pour une compréhension des espaces ruraux : représentations du paysage de territoires français et québécois
Bibliographic record
Abstract
Understanding rural areas : an approach of territories through landscape in France and Québec Landscape reveals present day conception of space. And this will continue as landscape is strongly influenced by government policy in regard to territory. In certain rural areas, the landscape can counter the decline as a result of intensive agriculture and the changes in landscape over the past fifty years. While focusing on the relationship between the social, spacial and cultural aspects of landscape, we will use two examples, the Haut Saint-Laurent in Québec and Gâtine poitevine in France. We see how landscape has being mutated as a result of the industrialisation of agriculture and its subsequent consequences. It deals with the characteristics of these basic processes, i. e. the sudden change that followed the industrialization of agriculture and the consequences it implied. The effects on the landscape are varied and touch the heritage elements and that of the landscape imagined : that is the landscape resulting from social perceptions and representations – for example the hedges and the scrap-metal which are constantly losing ground in Gâtine. Or the agricultural demise on the plateau of the Haut Saint-Laurent easily witnessed through greater uncultivated and abandoned land left to waste to the benefit of intensive agriculture on the more fertile land of the plain. The question then arises which is the basis of our reflection : what link can one establish between changes in landscape and the territorial representations ? These problems were approached through a in-depth survey carried out on the two territories. The preliminary results presented in the following article invite us, through comparison of the two experiments, to revisit the concepts of landscape, cultural identity, territoriality and should provide us with the raw material to elaborate a theoretical structure of the relationship between society and place. We will explore the strains exerted on the landscape, and those between social groups, the direction of these reports/ ratios, all with the goal of giving a greater understanding of the organizational relationship social/ actions, collectivities/ impacts on the social ground and the objective sphere of the landscape.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".