Critical Pedagogy: Stem Cell Research as it Relates to Bodies, Labor and Care (Dispatch)
Bibliographic record
Abstract
As biomedical research has come to depend on human bodies, tissues, and cells, students pursuing life science education need to navigate ethical issues associated with biospecimen collection.These biological resources are valued differently depending on characteristics such as race, class, and gender.For instance, people with unique biological or physical characteristics can provide cord blood in exchange for educational scholarships or housing subsidies.In egg-sharing schemes the "donation" of a portion of one's human eggs to human embryonic stem cell research can result in reduced costs of assisted reproductive services (Dickenson & Idiakez, 2008).As a consequence of these public contributions of biospecimens, a delicate relationship between the public and private sector is evolving, raising questions about ownership, compensation, access, and privacy.Additionally, as genetic factors influence the value of these biospecimens, they also pose concerns about eugenics (Klitzman & Sauer, 2015).To address this changing landscape, an interdisciplinary team of scholars and activists embarked on a curriculum development project to promote scientific innovation and socially just practices.The product of this effort is Stem Cells Across the Curriculum (SCAC, n.d.), an open access educational resource that highlights the transactional nature of life science research by integrating the biological and social dimensions using a social justice framework. 1 1 Stem Cells Across the Curriculum (SCAC) may be accessed at www.stemcellcurriculum.org.To view SCAC Modules select "Curriculum and Cases."To view Artwork, Infographics or Presentations select "Media and Infographics."Specific URLs for web materials are provided in the reference list.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.014 |
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".