Knowledge Gaps and Opportunities for Future Research on Ethiopian Food Security and Agriculture
Bibliographic record
Abstract
Research is needed to support informed decision making and evidence-based policies, programs and services. Research on food security and agriculture in Ethiopia has contributed to significant advances made over the last century. The volume of research produced in these areas is vast; in 2016, this amounted to hundreds of publications each week, on average. In this article, we present a short communication about the trends and knowledge gaps in the food security and agricultural research fields, highlighting opportunities for future research and thought leadership. Systematic reviews can assess and synthesize what is published, while reflections of those engaged in the research fields can help to identify what is not published, or under researched. Our objective is to direct researchers toward areas where information is crucially needed and where contributions to knowledge may have significant impact. We explore four knowledge gaps, specifically in the areas of contextualization, integration, synthesis and intersections. Keywords: Ethiopia, Agriculture, Food Security, Research Trends, Knowledge Gaps
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 | 0.041 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".