Bibliographic record
Abstract
![Figure][1] PHOTO: © LUCASFILM/SUNSET BOULEVARD/CORBIS “Would creating an army of clones to battle droids be ethically acceptable? As the Chancellor Palpatine suggested to Anakin Skywalker, could creating life or indefinitely prolonging life be considered morally legitimate? If so, then why did the Jedi's moral code strictly prevent all forms of research on altering life itself and ban the acquisition of such knowledge?” I pause to allow my students to absorb these questions. They regard me, wide-eyed. Summoning Master Yoda, Obi-Wan Kenobi, or even—may the Force protect us!—the Emperor in a class on the ethics of biomedical research might at first sound a bit out of place. But upon closer look, the Jedi and the Sith could well be welcome there. Star Wars presents an almost continuous collection of life-related moral dilemmas, each of them serving as a perfect starting point for discussions in a biomedical classroom. From massive cloning and species destruction or creation, to mind-control and knowledge being kept by (and for) a limited elite, Star Wars provides examples for almost all of the challenges we want students to consider. The vast majority of students are highly familiar with the popular culture phenomenon that is Star Wars , and they are motivated to actively engage in the debate process. With biomedical science moving so fast that it might sound like fiction, one of our missions is to prepare students—the future scientists—for the ethical challenges they might encounter in their career. Popular culture is just a tool we can use to approach such essential questions. Still, awakening the Force in the classroom might help our students avoid becoming seduced by the Dark Side. [1]: pending:yes
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".