Grassroots Participation in Decision-Making Process and Development Programmes as Correlate of Sustainability of Community Development Programmes in Nigeria
Bibliographic record
Abstract
This study examines grassroots participation in decision-making process and sustainability of community development programmes in Nigeria. In spite of many policies on development programmes by the government, the physical and socio-economic conditions in most of communities in Nigeria do not seem to have improved significantly. The descriptive survey research design was used. The stratified random sampling technique was adopted to select 1,984 respondents (community leaders (266); change-agents (569); members of community development associations (1,022) and political representatives (127) in nine communities each from Osun (964) and Kwara states (1,020)). A questionnaire: Grassroots Participation in Decision-making Process and Programmes Scale (GPDPPS) and Community Development Sustainability Questionnaire (CDSQ) were used for data collection. One research question was answered and two hypotheses tested at 0.05 level of significance. Data were analysed using Pearson’s Product Moment Correlation. Results showed that there is significant relationship between grassroots participation in development programmes (r=.335**; p<0.05); decision-making process (r=.210**; p<0.05) and sustainability of development programmes. Furthermore, political instability, leadership problems, communal clashes, inadequate funding and poor accountability impeded sustainability. It was recommended that the problems of political instability, leadership, inadequate funding, communal clashes, accountability, and communication gap should be considered in grassroots decision making in development programmes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".