Datasets of socio-economic, demographic, entrepreneurship, and financial inclusion indicators of selected sites in Ethiopia: Addis Ababa, Dire Dawa, Shirka zone
Bibliographic record
Abstract
Ethiopia is known for having a large portion of its population living under national and international poverty lines. Exclusively the poverty is aggravated being accompanied by a high youth unemployment rate and severe inequality. Thus, these datasets are collected to develop the poverty and unemployment profile of the country with an emphasis on eastern and central regions. Principally the data targeted Addis Ababa: the capital city; Dire Dawa city council- eastern province of Ethiopia and Arsi Zone. The datasets contain demographic variables, household details, education, health & nutrition, employment, non-wage income, death profiles, housing detail, asset ownership, household infrastructure, water & sanitation, household monthly expenditure, saving trends, and social engagement. Besides, the dataset encompasses youth-specific core variables such as finance, unemployment, and entrepreneurship variables. In collecting these datasets, enumerators who have experience in digital data collection were involved. Those enumerators equipped with the digital device were provided two days of digital data collection training, involved in a pilot survey, and finally engaged in the actual data collection activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".