Plantwise Sustainability: Two Years on Follow-up assessments in six countries
Bibliographic record
Abstract
CABI implemented its global Plantwise programme from 2011 to 2020 to address smallholder farmers' plant health challenges with more than 200 partner organizations in around 30 countries.Two years after Plantwise funding ceased, a follow-up sustainability assessment was carried out in six countries: Nepal and Pakistan (Asia); Ghana, Kenya, and Malawi (Africa); and Jamaica (Caribbean).The aim of the assessment was to gain an understanding of Plantwise's legacy, or elements thereof, and how the country context has influenced what happened, positively or negatively, since the programme ended, as well as what the drivers and blockers are to sustainability.Information was gathered through interviews and group discussions with Plantwise partners, and a review of recent programme documents, country policies, selected literature, and Plantwise Online Management System (POMS) data.This was followed by in-depth conversations over Zoom with country study teams to discuss findings and key lessons.The country studies for this assessment show that sustainability is highly contextual: knowledge on national policy, institutional mandates, mode of operation, and available resources, is paramount to ensure that interventions fit with structures and capacities in a particular setting.The country cases also show the different ways in which a particular circumstance, or combination of circumstances, helped or hindered sustainability in one country, while a different set of circumstances was influential in another country.Sudden changes can undermine partnerships and achievements made, which makes it difficult to engineer or promote sustainability.Using a flexible, adaptive approach, in which opportunities are spotted and seized, is vital.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".