University’s Catalytic Effect in Engendering Local Development Drives: Insight into the Instrumentality of Community-Based Service Learning
Bibliographic record
Abstract
The context of this paper is Africa, where communities have historically looked up to universities within their locality to maximize their intellectual capital and knowledge creation to foster regional development. How well these universities are actively responding to the demands of economic and social development require attention. This paper reports an instrumental case study involving in-depth interviews and focus groups within a bounded locality in Cameroon to address what universities can do to enhance their contribution to local development. Findings suggest that whilst a university’s community-based service learning (CBSL) scheme can be ascertained as an instrument that can engender local development, this requires the fostering of relevant education for informed participation of different stakeholders in the framing but also firming up of CBSL objectives and processes. Furthermore, in order to optimize the prospect for local development instigated by CBSL activities, relevant stakeholders should go beyond short-term planning and adopt futuristic sustainable strategies. There is need to promote deeper dissemination, as well as follow-up on field findings for sustained implementation and outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".