Belonging and the Charter of Transdisciplinarity in International STEM Research
Bibliographic record
Abstract
This paper examines the lived realities of conducting a purposefully transdisciplinary, equity-focused international STEM research project during the COVID-19 pandemic. Using the Charter of Transdisciplinarity as an analytic lens, we explore how our team navigated visa delays, shifting political contexts, administrative turnover, and digital inequities while supporting marginalized undergraduate women in STEM. Through reflective prompts and critical event analysis, we show how belonging—understood as an active, ongoing practice—enabled us to move beyond disciplinary boundaries and confront entrenched forms of marginalization in STEM and academia. Technology simultaneously connected and divided us, requiring continual renegotiation of community membership. Dialogues around artificial intelligence served as key moments of transcultural exchange and vulnerability. We argue that transdisciplinarity is a human and relational endeavor that must be intentionally cultivated. Within STEM, it emerges not from the acronym itself but from practices that center humanity, resist othering, and foster collective flourishing. Keywords: transdisciplinarity, STEM education, belonging, transcultural collaboration, international research, equity in STEM, participatory action research, digital inequity, interdisciplinary collaboration
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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 | 0.042 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.102 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.009 |
| 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, 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".