Do socio-demographic groups report different attitudes towards water resource management? Evidence from a Ghanaian case study
Bibliographic record
Abstract
Abstract Understanding the influence of socio-demographic factors on attitudes towards water pollution mitigation measures could help provide good pointers in the design of effective water resources management policies. Yet, very few studies have examined this in the developing country context. Using quantitative methods to analyse survey data from Ghana, the main goal of the current study was to determine whether socio-demographic groups report different attitudes towards water resource management. Results show that females reported higher pro-environmental attitudes than men (and these differences were statistically significant). Additionally, the employed were found to have reported higher pro-environmental attitudes than students and the unemployed, however, we do not find evidence to support the influence of age and educational attainment. Notwithstanding the relatively limited sample, this work offers valuable insights into the different factors that could influence environmental attitudes. Further research is needed on how sociodemographic variables interact with other psychosocial factors to determine environmental attitudes. This could advance our understanding on how different social groups may respond to policies designed to promote pro-environmental behaviour and reduce water pollution.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".