Information on biodiversity and environmental behaviors: a European study of individual and institutional drivers to adopt sustainable gardening practice
Bibliographic record
Abstract
The identification of individual and institutional drivers regarding ecological transition of\nindividual behaviors has been widely studied in the literature. However, few studies report the\nspecific case of private gardening practices, even though it is particularly relevant when\ndiscussing lifestyle habits and ecological transition, due to the wide range of positive and\nnegative environmental externalities private gardens may generate. Using a European database\n(Eurobarometer 83.4), we estimate individual and institutional drivers of sustainable gardening\npractices. Our econometric approach takes the specificities of our data into account, by using a\ntwo-step approach combining a generalized Heckman model and a meta-regression, and allows\nus to highlight the importance of the accessibility to biodiversity-related information in the\nadoption of environmentally friendly behaviors. Differentiated trends between European\ncountries are tested using indicators on economic development, social capital and\nenvironmental performances. In conclusion, we provide some recommendations in terms of\npublic policies.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".