Context Assessment for Agroecology Transformation in the Tunisian Living Landscape
Bibliographic record
Abstract
The purpose of this Context Assessment is threefold: first, to characterize the environmental, social and economic and political contexts of the Tunisian ALL; second, to understand the data and information currently available in sub-region of the ALL, and third to characterize the extent to which agroecological principles are already being employed locally at the ALL levels. This report constitutes a basis of information and discussion to conduct the impact assessment. It is also valuable to all WPs in the Initiative as it provides critical quantitative or qualitative data and information regarding capacities assessment, policy influence, and other environmental attributes which can guide the initiative implementation and impact in 2023/2024. The present Context Assessment in Tunisia has been elaborated from primary and secondary sources of data. The primary sources of data are issued from focus groups and formal and informal interviews conducted in the targeted area between June and December 2022, as part of WP1 and WP4 activities. The secondary sources of data came from previous research and development projects, in addition to formal and grey literature or technical reports and policy documents. This report will be enriched with a household survey planned during the first quarter of 2023. This report contributes to Output 2.1. Baseline – current conditions of agricultural systems of small holder farmers in each ALL, Output 1.1 on establishment of the ALL, Output 4.1 on the identification of policies and local institutions and their role in the AE pathways.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".