Repurposing emergence theories: An interview with Andrew Pelling
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.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.035 |
| 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".