Functionality of the Land Certification Program in Ethiopia: Exploratory Evaluation of the Processes of Updating Certificates
Bibliographic record
Abstract
Ethiopia has implemented one of the world’s most cost-effective systems to document land holdings, the land certification system. After more than 15 years since its launch, questions have been raised regarding its functionality. Specifically, there are concerns about the process of updating land certificates, thus ensuring the certificates and the registry are up-to-date. This exploratory evaluation seeks to provide formative evidence regarding this question, and, if warranted, give direction as to where additional research is needed. We find that in some areas, the mechanisms for updating land certificates are functional and in other areas not. Based upon these findings, we suggest four areas for future research, namely: (1) assessing the extent of non-functionality on a broader scale, (2) investigating the causes of non-functionality and viable options for addressing the cases thereof, (3) how policy can best address uninheritable land due to its small size, and (4) evaluating the viability of the future of rural livelihoods and what services ought to be put in place to enable a transition that provides decent livelihood alternatives.
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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 | 0.085 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".