Determinants of women-owned micro- and small tourism enterprise growth in Bale Zone, South Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gender gaps in tourism entrepreneurship are wide in Ethiopia, and women entrepreneurs underperform more often than men entrepreneurs. This study aimed to explore the determinants that affect the growth of women-owned micro and small enterprises (MSEs) in the tourism sector. Stratified random sampling was used to select 238 women-owned enterprises and analyse the data collected from them using a logit regression model. The findings show that being an older operator and operating a micro- (instead of small) enterprise have a negative effect, but family size, operator’s experience, credit access, and training received by the operator have positive effects on growth. These findings give useful information to relevant stakeholders, the Government of Ethiopia, and governments in other African countries to promote and empower women to grow their tourism MSEs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it