Bibliographic record
Abstract
Ethiopia has one of the largest livestock populations in Africa. In 2016–2017, the share of live animals, leather, and meat in the total export of the country reached 9.6%. This paper aims to identify the determinants of the export of Ethiopian livestock products by means of vector autoregressive and vector error correction models. Multivariate time series is used to model the association between the products of the Ethiopian livestock export included in the study. Vector autoregressive and vector error correction models are used for modelling and inference. The results indicated the existence of a long term correlation between the volume of live animals, meat and leather exports. The volume of meat export is significantly affected by a lag occurring in the export of live animals in the short-run. Therefore, 3.7% of the shortrun imbalance in the volume of leather export is adjusted each quarter. It is suggested that the exporters of livestock products should properly utilise the Ethiopian livestock resources. On the other hand, the government should offer different forms of support to exporters, especially those focusing on exporting value-added products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".