Environmental Knowledge and Policy Sustainability: A Study of Pro Environmental Policy Support among the Southeast Nigerian Rural Communities
Bibliographic record
Abstract
Environmental policy sustainability as part of the Sustainable Development Goals (SDGs) agenda has appeared as one of the challenges to most developing nations such as Nigeria. This inadvertently has affected (SDG) agenda in Nigeria. Although many scholars have given attention to other dimensions of socio-economic policies/natural environment, the case of environmental policy sustainability has received virtually no attention in some regions such as southeast Nigeria. The study aptly captured the context of human behavioural disposition towards environmental knowledge and policy sustainability among the rural population in southeast Nigeria in the framework of Symbolic Interactionism/Environmental Responsible Behaviour, with the support of survey data. The study involved 1200 respondents from rural communities, while data collected through questionnaire instrument were analyzed using inferential statistics. The findings show strong positive correlation between familiarity with ecological harmony and support to pro public environmental policy (rho= .84, n= 1200, p<0.01), knowledge of the natural environment and support to pro public environmental policy (rho= .87, n= 1200, p<0.01), while environmental policy sustainability can be predicted by some crucial socio-demographic factors (p<.000). By implication, knowledge of the operation of the natural environment and government policy approaches and dimensions should be encouraged among the rural communities in the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".