AgSecure Africa Programme<sup>TM</sup>: A Blended Training Approach for Africa
Bibliographic record
Abstract
was developed in partnership with the South African Agricultural Research Council-Onderstepoort Veterinary Research (ARC-OVR) to train veterinarians, animal health technicians, researchers and laboratory personnel. Three blended courses consisting of both virtual and in-person delivery were provided with the intent of contributing to the better prevention, detection and control of infectious diseases of livestock and poultry of significant importance for the region with a strong emphasis on transboundary animal diseases. A "train the trainer" model of instruction was employed to equip participants with the ability to train and share knowledge with colleagues and small-holder farmers in their various communities and regions. The design of this program was to increase the capacity of veterinarians and veterinary diagnosticians to safely and accurately diagnose infectious livestock diseases and to also empower small-holder farmers with the knowledge needed to safely and securely manage their livestock and be a first line defense in the prevention and control of infectious livestock diseases. Quantitative and qualitative evaluations were used to measure the impact of the trainings which revealed significant increases in knowledge gains. Course materials were submitted and approved for accreditation by the South African Veterinary Council (SAVC) becoming the first international training program to achieve this. Approval of these courses led to licensed veterinarians and animal health technicians being awarded continuing professional development credits upon their successful completion of courses. A larger goal was to build training capacity, not only for South Africa, but also for the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".