The Role of Climate Change Research and Habitat Restoration Campaigns in Promoting Sustainable Environmental Conservation in Higher Education
Bibliographic record
Abstract
This study aimed to investigate the role of Higher Education Institutions (HEIs) in sustainable environmental conservation.Universities conduct extensive research on regional and global biodiversity, assessing the health, threats, and populations of various species.Universities worldwide are expected to provide practical learning opportunities, such as fieldwork, internships, and community projects, that emphasize sustainability and environmental conservation.The researchers tested two hypotheses on how scientific research and habitat restoration campaigns contribute to environmental conservation.The quantitative study collected data using a survey questionnaire.The participants were 92 higher education academic staff purposively selected from 5 universities in Mogadishu, Somalia.The correlation coefficients for studies on climate change and habitat restoration initiatives in relation to sustainable environmental conservation were 0.597 and 0.601, respectively.The study found that climate change research (CCR) and habitat restoration campaigns (HRC) had a significant effect on sustainable environmental conservation (SEC), as indicated by p-values of 0.02 and 0.01, respectively.The study also highlighted the need for further investigation into the factors contributing to climate change, including greenhouse gas emissions, deforestation, and industrial activities.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".