Joe Sornberger, Dreams and Due Diligence: Till and McCulloch's Stem Cell Discovery and Legacy
Bibliographic record
Abstract
Joe Sornberger wants people to know about the important contribution by Canadians Jim Till and ‘Bun’ McCulloch to stem cell science and medicine. He asserts that they deserve far more credit than they have received, and draws on interviews with a number of their students and colleagues to make that point. The book is a mix of introduction to the work and polemical advocacy for its importance, and much less a scholarly social history. The emphasis on asserting Canadian leadership in stem cell science does not mean that the book does not have merit. It is often worth looking at less familiar historical episodes and reflecting on their impacts. Sornberger gives us a short book that might better have been a tightly organised essay. The structure leads to considerable redundancy, so that we read the same thing over and over. The strongest sections look at the way James Edgar Till and Ernest Armstrong McCullough came together to study stem cells in mice. We read (repeatedly) that the two were very different, though each embraced mathematical and physical approaches to medical questions. Together, they carried out experiments with radiation of mouse bone marrow cells and gradually came to see the implications of the work.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| 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".