Systems Readiness for Improved Monitoring and Evaluation with Specific Reference to the Micro Small and Medium Enterprises Sector in Nigeria
Bibliographic record
Abstract
The Federal Ministry of Industry, Trade and Investment (FMITI) is mandated by law to provide support services and creating conducive business environment that supports the transformation of both small and large scale industries in Nigeria.The FMITI mandate and task is facilitated through its subsidiary, the Small Medium Enterprises Development Agency of Nigeria (SMEDAN).This is against the background that the parastatal will facilitate development (if well supported) by triggering production, employment opportunities and growth.Especially in Nigeria, where the informal sector employs more people than the formal sector, but with declining affluences of micro and small businesses, questions must be asked concerning the effectiveness of the institution's programmes and policies in revitalising, sustaining as well as growing the micro, small and medium enterprises (MSMEs) sector.In this paper, literature on monitoring and evaluation (M&E), legislative framework linked to the functioning of small and medium business sector is extensively reviewed.Furthermore, this paper will critically evaluate SMEDAN mandate to provide support services that will transform the informal sector of the Nigerian economy using existing monitoring and evaluation systems of selected programmes and policies put in place by the agency to indicate readiness (or lack thereof) of the current system to further develop the micro, small and medium enterprises (MSMEs) sector of the economy.This paper adopts qualitative and quantitative methodologies.It is anticipated that findings from this research-based paper will present lessons which can be harnessed to better reposition monitoring and evaluation systems hence, ensure effectiveness of future programmes and policies that will generate employment opportunities through SMEDAN.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".