Women Empowerment as a Mediator Between Environmental Conservation and Climate Intervention
Bibliographic record
Abstract
The study investigated the influence of environmental conservation on climate intervention and explored how women empowerment mediates between the both.Using survey questionnaire, 246 responses were collected from Saudi women.To understand these associations, Smart PLS-SEM version 4 was employed for data analysis.This study significantly supported all the hypotheses; environmental conservation has a positive significant impact on climate intervention (β = 0.552; p = 0.003).The research verified that women empowerment plays a mediating role, with statistical significance (β = 0.267; p = 0.015), highlighting their crucial function as key drivers in advancing sustainable development.The study's conclusion highlighted the value of women's empowerment as a link between combating climate change and environmental preservation.Saudi women are found to have sufficient knowledge on environment and have mobility in resources, and be good decision-makers.By identifying and leveraging the unique talents of women, policymakers and practitioners may develop more inclusive and effective strategies for decreasing environmental challenges and building climate resilient communities, thereby helping to realize the goals of Saudi Vision 2030.This study contributes to the ongoing discourse regarding the interplay between gender, climate resilience, and environmental sustainability by endorsing all-encompassing approaches that empower women to be agents of change.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".