The Socio-Economic Benefit of the Livestock Traceability System on Communal Beef Farmers in Swaziland
Bibliographic record
Abstract
In this article, Swaziland is placed in the forefront as a small African country that implemented a livestock traceability system to benefit both communal and commercial farmers. The communal farmers are also able to export beef to European countries, markers that were previously unavailable to them, due to the successful implementation of the Swaziland Livestock Information and Traceability System (SLITS). Livestock traceability is briefly explained to align it with the importance of safe food production for human consumption and a few aspects are highlighted. The traceability systems is further explained in terms of its benefit to the rural economy, its role in growing the GDP and the realization of its aims as was initially expected by the Swazi Government. The data collection methods used were a document review, a case study and five interviews. It is concluded that livestock traceability systems should be adopted wider by other developing countries as it has a direct effect on the improvement of the socio-economic conditions of the rural poor. Its development and implementation remains very expensive, but Swaziland can be used as an example of a country that is able to reap the rewards from a commodity that is ample in their country, but scarce globally, leading to wider food sustainability.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".