Pro-environmental behaviour impacts and their relationship to insurance claim frequency through individual household and municipal-level analyses
Bibliographic record
Abstract
The primary objective of the research has been to determine the relationship between pro-environmental behaviour (PEB) and risk-mitigating behaviours. Chapter 2 approached the objective by comparing a direct measurement of individual household behaviours and motivations to insurance claim frequency scores. Chapter 3 approached the objective by measuring municipal actions based on milestones completed for carbon mitigation as an indirect proxy for pro-environmental behaviour at a municipal level. The milestone data was then compared to the same insurance claim frequency scores. The outcome of both studies did not identify a clear link between pro-environmental and risk-mitigating behaviour through behavioural spillover. Instead, Chapter 2 found that at a community level data resolution, age, income, education, or place of residence do not influence the PEBs of an individual. Also, Chapter 2 found that the intentions of an individual do not reflect their behaviour. Chapter 3 models did not show significant evidence of any relationship between the milestone data and the frequency of insurance claims for a municipality, indicating an absence of spillover. This study suggests that within the bounds of such a program, municipalities are experiencing either a lack of motivation for the initial behaviour or barriers to subsequent behaviours are too large. Considering both papers, in order to fully assess and understand the relationship between PEBs and risk-mitigating behaviour, additional research is necessary.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".