Road to Kamaka: the struggles of poverty and desertification
Bibliographic record
Abstract
Subject area Sustainable development (in under-developed rural communities). Study level/applicability Bachelor's degree. Case overview The case follows six young adults from Quebec, who are mandated with a three-month agro-environmental project in the fight against desertification and poverty, in Kamaka, a village in the Sahel region of Mali. The project's central element is the development of a community garden that would ensure the diversification of the community's nutritional diet, and the rehabilitation of the environment. The mandate also consists of various environmental awareness workshops pertaining to efficient energy consumption, composting, and solar food drying techniques. The project, in its fourth year of collaboration between the Quebec organization and their local Malian partner, does not seem to have been yielding the desired results. The team is faced with the challenges of understanding the opportunities and limitations of the project so that they can try to succeed where previous teams have failed; while overcoming the organizational and logistical shortfalls that they faced prior to the start of their work, as they simultaneously struggled to adapt to their totally new context. Expected learning outcomes How to prepare for, approach, and carry out local community development projects – environmental and/or social – in under-developed regions such as Mali. Mainly, how to create a shared vision with the concerned community; build an effective multi-stakeholder network; and ultimately co-create sustainable value (as per the proposed Senge model). Supplementary materials Teaching notes and short documentary online link.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".