Rural Peoples’ Perception to Climate Variability/Change in Cross River State-Nigeria
Bibliographic record
Abstract
The rural people have been recognized as knowledge holders on climate variability/change and key actors for developing policies to mitigate and cope with its effects. The study attempts to assess perception level of rural people to Climate change in selected communities in Cross River State, Nigeria. Primary data were collected from 120 rural dwellers in 4 communities. This data centered on knowledge (awareness) level of climate variability/change causes, effects, mitigation and adaptive strategies. The data generated were analyzed using the descriptive statistics. Results showed 71.7% of the people are aware of climate change. They also indicated that the onset of rains is now delayed while cessation is earlier against the trend in the past. This corroborates the meteorological parameters obtained from Nigeria Meteorological Agency. The Study further indicates that, though there are natural causes, 66.7% of rural people accepted human activities as major causes of climate change/variability. The results also showed that the effects of climate in rural areas include poor crop yields (56.7% response); reduced soil fertility (66.7% response); increase flood (56.7%), poverty and food shortage (50% response). The sources of peoples’ awareness show widespread information from environmental education/sensitization by NGOs and extension workers as well as media which at the moment is lacking and limited to radio talks and jingles. It is recommended that the more awareness should be created on the effect of human activities on climate; also, indigenous knowledge system should complement global modern knowledge systems to enhance climate change mitigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".