Building a Strategy to Overcome the Psychological Barriers to Climate Change Management in Rural Communities of Fako Division, Cameroon
Bibliographic record
Abstract
This study seeks to build a strategy to overcome the psychological barriers to climate change management for rural communities in Fako Division. We employ a five point likert scale in which 100 inhabitants (adults) were surveyed purposefully surveyed in four rural communities (Malende, Bakingili, Bokwai and Miselele) of Fako Division (25 for each community) to identify the observed barriers. Based on the mean values derived from the 5 point likert scale, the study revealed that ignorance (mean=3.27) was the highest psychological barrier while denial stood as the least (mean=2.25). We then, as a recommendation, proposed a strategy for overcoming these psychological barriers which suggests that the government, the councils, NGOs, traditional authorities and the local population should collectively work together to identify people’s socio-economic needs and improve climate change management by empowering the population through workshop sensitisation, seminars and the use of the local media to reduce ignorance. Also, we suggest that they should motivate and create a number of incentives which would assist in reducing these observed barriers so as to ensure that developmental activities should respect stricto senso, issues of climate change management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".