Citizens’ attitudes toward the protection of flying squirrels in urban areas
Bibliographic record
Abstract
The Siberian flying squirrel (Pteromys volans) is included among the strictly protected species of the Habitats Directive (92/43/EC) of the European Union, which is one of the key instruments for biodiversity preservation in Europe. Strict protection of the species has a potential to cause conflicts in areas where forest management and urban development compete for the same space with the flying squirrel. This study examined attitudes of Finnish citizens toward the protection of flying squirrels in urban areas using survey data collected in three cities: Espoo, Jyväskylä, and Kuopio. Two samples (random and self-selection samples) were collected to investigate how the specific process of giving “voice” to citizens by polls in urban planning affects the results. The analysis was conducted by integrating factor and cluster analysis and multinomial logistic regression modeling. Four attitude groups of citizens were identified and named: “neutral on protection” (share of respondents: 33%), “strongly in favor of protection” (32%), “somewhat against protection” (26%), and “strongly against protection” (9%). Several individual-specific factors were found to be associated with the probability of belonging to different attitude groups. For example, female respondents had a higher probability of belonging to the group that was strongly in favor of protection, and older respondents had a higher probability of belonging to groups against protection. Respondents of the self-selection sample had a higher probability of belonging to the “strongly in favor of protection” group. They therefore had a more positive attitude toward the protection of flying squirrels than the other respondents. This finding indicates that cities may gain an overly positive view of citizens’ attitudes toward the protection of flying squirrels through current public participation methods based on self-selection procedures, such as public hearings used in land use planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".