Comparing social representations of the landscape: a methodology
Bibliographic record
Abstract
Social representations (SRs) are systems of values, ideas, and practices that characterize individuals' and social groups' relationships to both their social and natural environment. Comparing SRs between places, social groups, and through time is critical to understanding how social-ecological systems (SESs) and their diverse uses are perceived, interpreted, and understood. This knowledge needs to be taken into account to achieve efficient land use management of SESs such as agricultural landscapes. People's perceptions of the landscape are increasingly studied in sustainability sciences and a growing number of studies use the SR framework for analyzing differences in SRs between stakeholders and localities or for detecting changes over time. Robust methodologies able to compare SRs are required for this purpose. In this paper, we propose a modular approach to studying SRs from words collected from free listing tasks. This approach relies on standardizing definitions of frequency thresholds commonly used to assess SR content, consensus level, and structure. We then illustrate the value of this methodological approach through a comparative study of farmers' social representations of the agricultural landscape among four contrasted social-ecological contexts in France. We show how our comparative method allows for characterizing spatial variations in SRs and identifying social-ecological factors that influence the structuration and content of SRs. Finally, we discuss our methodological progress and the implications of our results for public policies aimed at managing SESs and in particular agricultural landscapes for conservation.
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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 | 0.046 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".