Strengthening collaborative capacity: experiences from a short, intensive field course on ecosystems, health and society
Bibliographic record
Abstract
A key capacity for engagement in the emerging field of ecohealth is the ability to work collaboratively. Between 2008 and 2010, the Canadian Community of Practice in Ecosystem Approaches to Health collectively designed and delivered three foundational, intensive, field courses. This paper presents findings derived from both quantitative and qualitative student course evaluation survey data. New insights arise around: the diverse opportunities for learning collaboratively in order to tackle complex socio-ecological issues, the social dynamics of collaborative relationships and learning, and the learning challenges that arise during intensive field courses. The lessons learned from these foundational years have enhanced understanding of the interrelated contributions to collaborative learning and relationship building and their relevance to addressing issues spanning ecosystems, health and society.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".