Large‐Scale Transdisciplinary Collaboration for Adaptation Research: Challenges and Insights
Bibliographic record
Abstract
An increasing number of research programs seek to support adaptation to climate change through the engagement of large-scale transdisciplinary networks that span countries and continents. While transdisciplinary research processes have been a topic of reflection, practice, and refinement for some time, these trends now mean that the global change research community needs to reflect and learn how to pursue collaborative research on a large scale. This paper shares insights from a seven-year climate change adaptation research program that supports collaboration between more than 450 researchers and practitioners across four consortia and 17 countries. The experience confirms the importance of attention to careful design for transdisciplinary collaboration, but also highlights that this alone is not enough. The success of well-designed transdisciplinary research processes is also strongly influenced by relational and systemic features of collaborative relationships. Relational features include interpersonal trust, mutual respect, and leadership styles, while systemic features include legal partnership agreements, power asymmetries between partners, and institutional values and cultures. In the new arena of large-scale collaborative science efforts, enablers of transdisciplinary collaboration include dedicated project coordinators, leaders at multiple levels, and the availability of small amounts of flexible funds to enable nimble responses to opportunities and unexpected collaborations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.055 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".