Human and Institutional Capacity Enhancement for Environmental Management in Indonesia: The Experience of the University Consortium on the Environment, 1987–2002
Bibliographic record
Abstract
The University Consortium on the Environment involved collaboration among Waterloo, York, and seven Indonesian universities in Java and Sulawesi in a comprehensive effort to enhance the capacity of Environmental Studies Centres in Indonesian universities to support good environmental management. Focussing on graduate student education (for both Indonesians and Canadians), joint faculty research, workshops, and the preparation of academic publications and practical training manuals, this large, complex project yields a number of important lessons. Important factors relating to success include forms of participation, leadership and management, institutional relations and structures, communication, and cross-cutting themes for environmental research, training, and management. The experience demonstrates the particular values and challenges of undertaking such projects in Canadian universities.
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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 | 0.001 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".