Identifying transformational space for transdisciplinarity: using art to access the hidden third
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A challenge for transdisciplinary sustainability science is learning how to bridge diverse worldviews among collaborators in respectful ways. A temptation in transdisciplinary work is to focus on improving scientific practices rather than engage research partners in spaces that mutually respect how we learn from each other and set the stage for change. We used the concept of Nicolescu's "Hidden Third" to identify and operationalize this transformative space, because it focused on bridging "objective" and "subjective" worldviews through art. Between 2014 and 2017, we explored the engagement of indigenous peoples from three inland delta regions in Canada and as a team of interdisciplinary scholars and students who worked together to better understand long-term social-ecological change in those regions. In working together, we identified five characteristics associated with respectful, transformative transdisciplinary space. These included (1) establishing an unfiltered safe place where (2) subjective and objective experiences and (3) different world views could come together through (4) interactive and (5) multiple sensory experiences. On the whole, we were more effective in achieving characteristics 2-5-bringing together the subjective and objective experiences, where different worldviews could come together-than in achieving characteristic 1-creating a truly unfiltered and safe space for expression. The novelty of this work is in how we sought to change our own engagement practices to advance sustainability rather than improving scientific techniques. Recommendations for sustainability scientists working in similar contexts are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it