I Come From: Using Collaborative Auto/Biographical Poetry to Foster Transdisciplinarity and Build Inclusion
Bibliographic record
Abstract
Transdisciplinarity refers to ways of working that bring together people from different backgrounds—academic and nonacademic—to address real-world challenges. This article explores how team members on the IncludeAge project, funded by the UK Economic and Social Research Council, enabled transdisciplinary ways of working to build inclusion in the project, by designing and facilitating a collaborative auto/biography poetry activity for transdisciplinary team members. The article demonstrates the potential for using collaborative creative writing to foster transdisciplinarity and build inclusion within the context of a research project with multiple team members from different backgrounds. The article aims to contribute to methodological discussions on transdisciplinary ways of working in research, particularly with seldom-heard populations, and how creative methods such as auto/biography and collaborative poetry writing may contribute to these.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".