Processes of Social Learning in Sustainability Research Teams
Bibliographic record
Abstract
In Canada and in many other countries, there is a clear commitment to address environmental sustainability and tackle global change through effective collaborations among discipline specialists, resource-users, and funding agencies. Recognizing that effective cross sector collaboration and transdisciplinary (TD) integration is rare, we design a capacity building learning intervention with the overarching goal to train early career professionals currently involved in global change research in TD approaches for policy and science for sustainability in the Americas. We applied a mixed-method design to analyze data collected from participants attending this training event. The sample included 164 participants from 15 countries. Data included application files and sociometric surveys. Our findings offer empirical evidence for improving team science in global change sustainability research teams.
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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 | 0.040 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".