Reflecting on arts-based participatory research: considerations for more equitable transdisciplinary collaborations
Bibliographic record
Abstract
Although the importance of pursuing meaningful and equitable transdisciplinary research collaborations with Indigenous and local community members has been established in the literature, challenges remain as to how to best do this in practice. Pursuing arts-based participatory research methods in two different ocean governance contexts in South Africa, this paper provides reflections by social and marine scientists, Indigenous and local community members, and artists taking part in transdisciplinary collaborations as co-researchers and co-facilitators. Centralizing the use of arts-based methods in the form of storytelling and photography, we consider some key lessons emerging from this transdisciplinary research for transformative ocean governance. This includes the need to actively critique and disrupt the invented roles of “researchers” and “research participants” and to build strong relationships and trust prior to the envisioned research process. We argue that the use of arts-based participatory methods has supported meaningful learning across multiple ways of relating to and connecting with the ocean and highlight inherent barriers to truly collaborative transdisciplinary research that are relevant for projects in different contexts and at various scales, such as the inequity of academic publishing processes and ownership of knowledge outputs. Despite continuous difficulties in ensuring equitable valuation of various knowledge systems, we find that arts-based participatory processes are valuable in advancing what we refer to as “comprehensive transdisciplinarity,” where non-academic co-researchers take part in conceptualization, methods formation, and dissemination of the research. We propose some critical questions that can assist teams considering transdisciplinary collaborations and conclude with some lessons and recommendations for academic institutions to better support equitable transdisciplinary collaborations that are needed to advance deep transformations toward 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 | 0.538 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.039 | 0.102 |
| Scholarly communication | 0.048 | 0.059 |
| Open science | 0.011 | 0.055 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".