Wild leafy vegetable use and knowledge across multiple sites in Morocco: a case study for transmission of local knowledge?
Bibliographic record
Abstract
BACKGROUND: There are few publications on the use and diversity of wild leafy vegetables (WLVs) in Morocco. In order to address this gap, we conducted ethnobotanical field work in Taounate, Azilal and El House regions. METHODS: Ethnobotanical collections, free listing, qualitative interviews and a 7 day food frequency questionnaire. RESULTS: More than 30 species in 23 genera of WLV were identified. Of these 4 had not previously recorded as WLVs used in Morocco in the literature. WLVs were used by 84% of households surveyed in Taounate (N = 61, in March 2005), and were used up to 4 times a week. Qualitative data revealed both positive and negative perceptions of WLVs and detailed knowledge about preparation among women. The greatest diversity of WLV knowledge and use was in the Rif Mountains (Taounate). There was significant variation in nomenclature and salience of WLVs, not only between regions, but also between villages in the same region. Within the same region (or even village) different local names were used for a given species or genus, and different species were identified by the same local name (including species from different botanical families). Data showed greater overlap in knowledge among villages using the same market. CONCLUSION: We believe the results suggest that markets are important sites for WLV knowledge transmission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".