How to fix a broken heart: Cardiac disease and the ‘multiverse’ of stem cell research in Canada
Bibliographic record
Abstract
This article focuses on Canadian stem cell researchers working on therapeutic applications of autologous stem cells for heart disease. Building on the concept of ‘multiverse’ – coined by William James and then further developed by Ernst Bloch – we are interested in the simultaneity of the certain and uncertain, sometimes contradictory arguments articulated by these scientists. In the first part of the article we illustrate some of the factors that provide certainty for researchers and clinicians. The second part analyzes the ways in which uncertain elements become integrated into a discourse of certainty. What we would like to show, using the concept of multiverse, is that a relatively new bio-technology such as stem cell treatments generally relies on both certain and uncertain reasoning. However, uncertainty has to give way to a platform of partial certainty, if crucial action is to be taken on issues as diverse as treatments and grant applications. The principle mechanisms we found that can make this kind of transformation possible target future developments (what we call ‘if only arguments’), including past encouraging results in need of further research.
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 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.028 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".