Inclusive transdisciplinarity: embracing diverse ways of being and knowing through inner work
Bibliographic record
Abstract
Transdisciplinary research (TDR) aims to co-produce knowledge to address the complex challenges of unsustainability. Despite progress in articulating principles for successful co-production, Indigenous researchers have pointed out ongoing power imbalances. These disparities, partly stemming from unacknowledged ontological-epistemological inequalities, often perpetuate hidden hierarchies between researchers and participants. At the core of these power imbalances is the dominance in academia of certain ways of knowing (e.g., categorical, experimental, noun-based, substantialist) over others (e.g., relational, experiential, verb-based, idealist). This bias is formalized and reinforced by academic institutions and cultures, passed down and internalized through education and professionalization. Inclusive TDR needs to break this self-reinforcing cycle, but this requires making inner room for multiple perspectives on reality and existence. To explore how inner work may foster ontological pluralism and inclusive TDR, we held a workshop drawing lessons from three case studies of TDR from Malaysia, Botswana, and Ecuador. Participants’ experiences were synthesized into a reflexive cycle of five inner shifts toward inclusive TDR. These shifts enhance the ability of researchers to engage with different ontologies beyond scientific materialism, and recognize their embeddedness in various kinds of relationships, extending their relational awareness to other beings, human and non-human, living and non-living. The proposed reflexive cycle seeks to cultivate capacities for co-production in TDR that are grounded in horizontally inclusive research practices that allow for more contextually relevant and impactful solutions to complex real-world problems.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.086 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".