An Integrated Approach to Capacity Development in Forestry and Climate Change in West Africa
Bibliographic record
Abstract
The BIODEV capacity development programme (BCDP) uses forestry, agroforestry and trees to derive a broad range of development and environmental outcomes (high-value biocarbon) while strengthening the capacities of local and national institutions to be able to sustain the benefits. The BCDP conducted 40 long and short-term training activities in Burkina Faso, Guinea, Mali and Sierra Leone across the following categories: (i) Short-term in-country trainings and regional training e.g. support to UNFCCC negotiators running from 2-4 and 6-8 days and implemented using the Harvard Case and the Socratic Learning Methods; (ii) Short-term training abroad for three weeks on Managing Sustainable Forest Landscapes, and (iii) Long-term training abroad e.g. PhD study on forest governance and climate change. The success of BCDP is largely influenced by (i) the effectiveness of coordination and planning amongst trainers; (ii) the content, format, depth, focus and duration of the training vis-a-vis the needs of the trainees; and (iii) the strengthening of existing local and national decision making and implementation platforms for up-scaling high value biocarbon development approaches.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".