Role of Environmental Literacy Education in the Realization of SDG6 in Rivers State of Nigeria: A Case Study
Bibliographic record
Abstract
Sustainable Development Goal 6 (SDG6) was adopted by the United Nations (UN) in 2015 for the purpose of ensuring availability and sustainable management of clean water and sanitation for all humanity by the year 2030. The UN considers realization of SDG6 an issue of utmost concern to governments and citizens all over the world. The purpose of the research study reported in this paper was to crystalize the role of Environmental Literacy Education (ELE) in the process of SDG6 realization in Rivers State of Nigeria. Two Local Government Areas (LGAs) of the State were used for case study. Two Research Questions and a Null Hypothesis guided the study. A questionnaire was used to provide data needed for the study. The population of the respondents was 7,717. A random sampling technique was adopted to select 20% of them for the study. Mean responses and the T-test were employed to analyze obtained data. The findings revealed general inadequate provision of clean water and effective sanitation management due to very low impact of the activities of Stakeholders entrusted with achievement of SGD6 in Rivers State. To remedy the lapses discovered, the researchers delineated five situation-specific ELE programmes, integrating relevant aspects of the UN Education 2030 Agenda for SDG6. The programmes clearly portray the vital role of ELE towards achievement of SDG6 and have thus been recommended, with their modes of implementation, for adoption by the Rivers State.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".