Repurposing emergence theories: An interview with Andrew Pelling
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
Andrew Pelling is a Canadian experimental scientist who uses low-cost, open source materials to create the medical technology of the future. He runs an interdisciplinary, curiosity-driven lab at the University of Ottawa ( pellinglab.net ), where he researches non-genetic ways to create artificial tissues and organs. Much of his experimental work has led to new insights in cancer pathology, muscle degeneration and stem-cell development. He has a cross-appointment in the departments of Physics and Biology and the Institute for Science, Society and Policy at the University, has held a Canada Research Chair since 2008 and was elected a member of the Global Young Academy in 2013. He is an honorary research fellow at SymbioticA, Center of excellence for biological arts. Dr Pelling has also recently started a company to sell and distribute low-cost kits for key scientific equipment that lets anyone create biomaterials for regenerative medicine. His latest achievements and hard work have earned him a place in the TED2016 Fellows Class. We were interested to interview Andrew Pelling, whose experience within and beyond the life sciences could help us better navigate the complex and emerging realms of laboratory life.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| 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