Social and Economics Perspective of Rural-Urban Migration: A Case Study Middle and Lower Shabele Regions in Somalia
Bibliographic record
Abstract
The research is conducted in two major regions of Somalia: Lower and Middle Shabele in order to get a deep insight in the idea of socio-economic rural-urban migration patterns.People from rural areas prefer to move toward big cities under different socioeconomic factors.This research study revolves around the concept of rural-urban migration from the countryside of Somalia.It is briefly defining the concept of rural-urban migration.As urbanization is the process by which rural communities starts growing in an urban area or cities for the growth of expansion, for their standards of living, for excellent health facilities, for high education, jobs and several other social and economic reasons.The target areas from Lower and Middle Shabele are: Marka, Barawe, while from Afgoi Province the researcher will include Johar, Balcad, because these are the cities where people used to migrate immensely.The sample size will be 100 people from different areas.This research will be based on quantitative.Data is collected through questionaire.Results will be presented in tabulation and percentage form.The suggestions will be presented by the researcher according to the results of the data.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".