Communities of Practice in Crop Diversity Management: From Data to Collaborative Governance
Bibliographic record
Abstract
Abstract Establishing linkage among data of diverse domains (e.g. biological, environmental, socio-economical, and geographical) is critical to address complex multidimensional issues such as food security or sustainable agriculture. The complexity of this challenge increases with the level of heterogeneity of the data but also with the social context of production of datasets, a dimension usually less considered. Building on the experience of a transdisciplinary project on the diversity of crop diversity management systems in West Africa (CoEx), this chapter reflects on the importance to better account for agency for more meaningful, responsible and efficient plant data linkage. The chapter addresses sequentially the cognitive and political challenges related to data work and the way they could be addressed simultaneously within the same social unit. To do this, we rely on the concept of community of practice (CoP) which gained enormous popularity in relation to data and knowledge management. More than simply a social mechanism for community knowledge management, we show in this contribution that CoP needs to be approached as a social experiment and a terrain of collective situated learning in order to address each challenge and their linkages with respect to data work.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".