Transdisciplinarity 101: Short-Term Training in Knowledge CoProduction to Face Global Environmental Change
Bibliographic record
Abstract
The Belmont Forum and the Inter-American Institute for Global Change Research (IAI) organized an online training workshop on transdisciplinary (TD) approaches at the Sustainability, Research, and Innovation Congress (SRI) in 2022. The IAI is an intergovernmental organization that brings together 19 countries from the Americas to support adaptation to the world’s changing environment. The Belmont Forum is a consortium of major funders and international science councils to promote knowledge about sustainability science. The workshop aimed to create a safe environment for participants to share their impressions of and experiences about transdisciplinary research, using the Americas (IAI mandate) as a launching point for TD approaches globally. The workshop consisted of two online sessions: Transdisciplinary Approach 101 and Transdisciplinary Case Studies. The objectives of the current workshop report are: 1) to identify the key takeaways regarding common challenges and opportunities for transdisciplinary practice among workshop participants’ experiences, upon which to base recommendations for best practices, e.g., managing power imbalances, conflicting priorities and timeframes, enhancing communication and consolidating contextual awareness. 2) to offer insights to build better strategies for “train the trainers'' processes around transdisciplinarity, especially in congresses and short-term events, including using an experience-based approach, offering specific tools and increasing the participation of non-academic partners. This report encourages the implementation of other training processes by experienced transdisciplinary researchers, practitioners, and funders, in order to build capacities for collaborative approaches in diverse scientific communities.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".