Ambiguous cells: the emergence of the stem cell concept in the nineteenth and twentieth centuries
Bibliographic record
Abstract
This paper elucidates the origins of scientific work on stem cells. From the late nineteenth century onwards, the notion of stem cells became customary in scientific communities of Imperial Germany. Adopting the term Stammzelle from Ernst Haeckel, Theodor Boveri was influential in introducing the concept in embryological studies and early genetics around 1900, describing a capacity of stem cells for self-renewal as well as differentiation. At the same time, blood stem cells were conceptualized by histologists such as Ernst Neumann and Artur Pappenheim in studies of physiological haematopoiesis and various forms of leukaemia. Furthermore, building on Julius Cohnheim's theory that tumours arise from 'embryonic remnants' in the adult body, pathologists aimed at identifying the cells of origin, particularly in the embryo-like teratomas. Embryonic stem cells thus assumed an ambiguous status, partly representing common heritage and normal development, and partly being seen as potential causes of cancer if they had been left behind or displaced during ontogeny. In the 1950s and 1960s experimental research on teratocarcinomas by Leroy Stevens and Barry Pierce in the USA brought together the strands of embryological and pathological work. Alongside the work of Ernest McCulloch and James Till at the Ontario Cancer Institute from the early 1960s on stem cells in haematopoiesis, this led into the beginnings of modern stem cell research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".