Taking a learning approach to community-based strategic environmental assessment: results from a Costa Rican case study
Bibliographic record
Abstract
This paper describes an innovative approach to environmental assessment that built local capacity enabling a more sustainable management of natural resources. It presents learning outcomes from a community-based strategic environmental assessment (CBSEA) involving communities from two Costa Rican watersheds who assessed the Instituto Costarricense de Electricidad's (ICE) proposed agro-conservation programme. Participants were engaged throughout the CBSEA process, from planning to the implementation of four highly interactive workshops representing steps in a strategic environmental assessment. Instrumental learning results included: learning about CBSEA and its role in programme planning; developing problem-solving skills related to assessing impacts and creating mitigation strategies; effective group-working strategies; and technical information. Communicative learning outcomes included becoming more self-aware, and appreciating environmental conservation and collaboration. ICE learnt a participatory methodology and reconsidered communities' role in programme planning. Findings contribute to understanding the process of adult learning in cross-cultural contexts and the link between individual learning and social action.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".