Bibliographic record
Abstract
It has long been recognised that complex real-world problems cannot be solved by one discipline working alone. In response, over recent decades, within both the academic community and funders, there has been an increased call for research projects that integrate different academic disciplines and non-academic partners—within this work, we refer to this as transdisciplinary (TD) research. Although there is increased attention to TD research, the literature also recognises that there are challenges in bringing together a diverse group with different perspectives and ways of thinking. One of the ways that has been suggested to unite the different domains is through boundary objects—tools, objects, or documents that help to create a mutual understanding or framing. Within this presentation, we will share how the Made Smarter Innovation: Centre for People-Led Digitalisation (PLD) explored the use of a song as a means to increase the unity of its transdisciplinary community and to disseminate its research to the wider public. The presentation explains the method of co-creation through to the final performance of the song and evaluation. In conclusion it reflects on the increased demand from the funders to demonstrate public engagement and briefly explores the suitability of the metrics which have been chosen to evaluate the success of the initiative.
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.
How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".