Aging in the Era of Regenerative Medicine: Analysis of Aging-Related Representations among Canadian Researchers
Bibliographic record
Abstract
Explicitly aimed at understanding and controlling molecular and cellular processes at the root of senescence and biological aging, regenerative medicine aspires to artificially reproduce the biological processes that enable the body to regenerate itself. This no longer involves conserving the body's state of balance by combating disease, as in clinical medicine, but rather fighting degeneration itself. From stem cell research to gene therapy to the production of replacement tissues, regenerative medicine perfectly corresponds to the logic of biomedicalization specific to postmodern society. Based on a series of 18 interviews conducted with Canadian researchers and clinicians in the field of regenerative medicine, this article seeks to understand representations of the aging body among researchers in this field. Seen from a strictly negative angle, aging is assimilated by researchers to an inevitable catastrophe that nevertheless must be combated. More closely observing the theoretical model of regenerative biology and the types of treatments developed, it can be observed, however, that this medicine of the future does not target the elderly, but rather promises youth the ability to regenerate themselves to avoid aging.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.047 | 0.021 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".