Bibliographic record
Abstract
A significant amount of research has been conducted on food security in Ethiopia, yet few reviews and syntheses areavailable. This paper reviews the research indexed on the Web of Science platform from 2005 until 2016 on foodsecurity in Ethiopia. It presents a summary of research, analyzes trends and outlines knowledge gaps as well aspotential areas for future research. For improved readability, the review categorized and synthesized research intoeight thematic research areas: (1) climate change and rainfall, (2) food science and technical agricultural studies, (3)inequalities, (4) individual-level studies, (5) large-scale land acquisitions and land grabs, (6) natural resourcemanagement and water, (7) social services and policy, and (8) vulnerability assessments and methods. The resultssuggest that while important research is being done, there is a greater need to expand our research on inequalities, toengage with new manifestations of food insecurity, to critically reflect on our measures and metrics of food security,and to engage in interdisciplinary approaches. Regular reviews and syntheses of the literature are required to betterenable researchers to build upon existing knowledge to identify key knowledge gaps and new research directions.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".