Challenges Facing Women Owned Micro Enterprises When Accessing Business Information in Limuru Constituency, Kenya
Bibliographic record
Abstract
The researchers focused on the challenges faced by women owned micro enterprises in accessing business information in Limuru Constituency, Kenya. Survey research design was used. The target population included 1764 women owned micro enterprises. Stratified random sampling and focus group discussions were utilized. The researchers found that the women respondents were fairly well educated as 77.2% of the women interviewed had secondary and post secondary education. Lack of information systems presented the biggest challenge at 76.5% followed by information available being expensive (71.6%) and business information available not being applicable to the local context (66.8%). To a lesser but still significant degree, outdated information, lack of knowledge on the availability of information and lack of relevant skills to access the information were identified. Information that was most sought after was in finance/credit, markets and investments while the least sought after was on taxation and insurance. Through cross tabulation and correlation analysis, it was found that there was minimal relationship between most of the factors above. The conclusion was that while an area may be in a rural setting, having an information system which disseminates relevant information may be a greater determinant of access to information than the geographical location.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".