Public Engagement in the Great Lakes Basin: a Regression & Spatial Analysis of Predictors of Public Engagement using Great Lakes Regional Survey Data
Bibliographic record
Abstract
This research project is an analysis of public opinion in the Great Lakes region (i.e., socio-demographic characteristics, values and beliefs) and an assessment of how these characteristics are related to watershed governance actions using LASSO regression and spatial analysis. The Great Lakes Regional Poll (GLRP) is a public opinion survey of 4550 residents (n = 4550) from across the GL basin performed on behalf of the International Joint Commission, and has been the focus of previous social science scholarship and analysis. The biennial survey includes over 60 questions related to perceived status and conditions of the Great Lakes, willingness to engage in water protection actions and questions about individual beliefs and policy/regulation support as well as respondent socio-demographics. The 2018 edition of the survey was analyzed previously using LASSO regression to determine which variables are significant related to public engagement. This study builds on previous research by analyzing the 2021 survey and consistency of results are discussed within the context of the Pro-Environmental Behaviour (PEB) literature. Geo-spatial mapping of residuals is also introduced as a diagnostic tool for model performance. The model includes 61 predictors from 2021 GLRP. Four consistent predictors were identified, including: Political Ideology, Indigenous Identity, Belief of Role of the Individual in GL protection; and Age. Two of these (Political Ideology and Indigenous Identity) are found to be consistent with previous work. The findings from this research related to these predictors gives insight for policy makers as well as researchers to develop theory and strategies related to future public engagement in the Great Lakes region.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".