Information and communication technologies as drivers of growth: experience from selected small-scale businesses in rural southwest Nigeria
Bibliographic record
Abstract
The pervasiveness of Information and Communication Technologies (ICTs) from cell phones to low cost videos, digital cameras, television, internet and laptops to mention just a few has transformed the lives of many especially the rural folks. Recent advances in ICTs have resulted in expanding existing jobs and creating new opportunities some of which were unimaginable only a few decades ago. The result has been significant worldwide and especially in developing countries like Nigeria where over three-quarter of her inhabitants rely on small-scale non-farm businesses and agriculture as means of livelihood. ICTs have therefore become an indispensable tool in fighting poverty and actualizing pro-poor growth. The result presented here relied on data collected from a random sample of 350 small-scale business owners in Southwest Nigeria. Analysis of respondents� socioeconomic characteristics revealed average age to be 44 years. Respondents� distribution by educational status showed that only about one-quarter were educated up to tertiary level with about 43% having no formal education. Occupational distribution of respondents showed farming as the highest employer of labour providing livelihood for over 67% of those interviewed. Analysis of available ICTs facilities revealed mobile phones, internet, television, radio, video cameras, digital cameras and cam-coders as the most widely used. Results further indicated cost reduction, ease of marketing and a quantum leap in the incomes of small-scale business owners as outcomes of ICTs penetration in the study area. Ranking of livelihood activities showed that small-scale businesses like trading, carpentry, brick-laying, barbing/hair-dressing, okada riding, carhiring, video clubbing, and vulcanizing as the most patronized. It is therefore suggested that effort should be intensified at building capacity of respondents through education. This is because education enhances adoption of new technologies and earning potentials of people. Improvement in the existing state of infrastructures should be made a policy priority to enhance accessibility to and affordability of ICTs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".