Obedience with Pauline Shanks Kaurin
Bibliographic record
Abstract
Overview & Shownotes There’s perhaps no better example of an obedient person than a soldier. And yet, soldiers often thoughtfully disobey direct orders, and in some cases, are legally obligated to disobey the rules. Pauline Shanks Kaurin, who is a philosopher and professor of military ethics at the U.S. Naval War College joins us to explore the ethics of obedience. She’s discussing her book On Obedience: Contrasting Philosophies for the Military, Citizenry, and Community. For the episode transcript, download a copy or read it below. Contact us at examiningethics@gmail.com Links to people and ideas mentioned in the show Pauline Shanks Kaurin, On Obedience: Contrasting Philosophies for the Military, Citizenry, and Community Mỹ Lai massacre Hugh Thompson Alasdair MacIntyre, After Virtue Martin Luther King, Jr., “Letter from a Birmingham Jail“ Thomas Aquinas on unjust laws USS Theodore Roosevelt and COVID-19 Credits Thanks to Evelyn Brosius for our logo. Music featured in the show: “Gin Boheme” by Blue Dot Sessions “Calgary Sweeps” by Blue Dot Sessions
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".