Bibliographic record
Abstract
Urbanization, as a major demographic trend, has become a global phenomenon. The year 2007, being taken as a tipping point where global urban population outnumbered the rural one for the first time, has been followed by increase in the magnitude of regional and global urbanization. Ethiopia, with 17% urban population in 2013, has also been urbanizing fast. Its urban growth implicates, inter alia, increase in urban areas of which Bahir Dar is among the most important ones. The study was, hence, conducted in Bahir Dar city to describe the impact of urban growth by purposively selecting four kebeles and 280 household respondents and by recruiting seventy informants. The study used multiple research methods including depth-interview, FGD, survey, and observation. Bahir Dar has been urbanizing fast. Its population size increased from 167,261 in 2005 to 249,125 in 2013 and total area from 28 Km2 in 2005 to 286.6 Km2 in 2006 and then after. According to the city administration, Bahir Dar is also witnessing high rate of urbanization, 6.4 percent in 2013. Built up area has also shown large increase, especially, over the past three decades. Focusing on urban growth in the city in the post revolution period, especially since the city’s designation as the capital of Amhara region in 1993, the study found that urban growth has been resulting in negative impacts including housing problem, informal settlements, pollution of ecosystem and water bodies, farm land encroachment, flooding, unemployment, poverty, and crime and so on.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".