Beyond Indicators and Success Stories: An Emerging Method to Assess Social Learning in Large-Scale Transdisciplinary Research Programs
Bibliographic record
Abstract
Facilitated learning approaches are increasingly being used as a means to enhance climate and sustainability collaborations working across disciplines, regions, and scales. With investments into promoting and supporting inter- and transdisciplinary learning in major programs on complex global challenges like climate change on the rise, scholars and practitioners are calling for a more grounded and empirical understanding of learning processes and their outcomes. Yet, methodologies for studying the interplay between learning and change in these initiatives remain scarce, owing to both the "hard to measure" nature of learning and the complexity of large-scale program implementation and evaluation. This paper proposes a new method for studying social learning in the context of large research programs. It aims to analyze the social learning of researchers and practitioners engaged in these programs and assess the contributions of this learning to the resilience of the natural and social systems that these programs seek to influence. We detail the theoretical basis for this new approach and set out six steps for developing multi-layered contribution pathways and contribution stories with stakeholders to document both the process and outcomes of social learning. The proposed method, we argue, can strengthen our analytical capacity to uncover the structural drivers and barriers to social learning that are often masked by the complexity of large-scale programs. An illustrative example, drawn from a large-scale climate adaptation research program, provides evidence on how this method might advance our methodological strategies for studying learning in these programs. We conclude by highlighting two key methodological contributions brought about through this approach, and by reflecting on opportunities for further methodological development. Enriching our understanding of learning and change processes, we argue, is an important avenue for understanding how we can pursue transformations for sustainability.
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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 | 0.066 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".