Pragmatic Secularism; Or, What <i>The Scarlet Letter</i> Can Teach Us about Modern Medicine
Bibliographic record
Abstract
This article argues that The Scarlet Letter (1850) offers a unique insight into American secularism’s inherent pragmatism—a pragmatism that attempts to provide a resolution for the healing arts’ struggle to be knowledgeable and moral at the same time. A “pragmatic secularism” continues to inform a particular brand of modern biomedicine, which can be seen in Atul Gawande’s medical reform text The Checklist Manifesto: How to Get Things Right (2009). Simply put, the pragmatic secularism found in these two very different books is a system of ascertaining right from wrong that relies on nothing more than someone’s work proving effective within the larger community. This article elaborates how these texts have a shared project in which a significant formation of the secular arises from situations that privilege practical applications—especially when urgent circumstances dictate immediate action as the only viable option. Inflected by the concerns of their respective historical moments, Hawthorne’s novel foregrounds the transformation of fringe knowledge into mainstream doing, whereas Gawande’s manifesto focuses on the limits of any kind of knowledge, privileging instead work as an end unto itself. However, the “power to do” exemplified by The Scarlet Letter as the core of the pragmatic secularist’s vocation finds its modern expression in The Checklist Manifesto’s fetishizing of medical work in a deliberate move to ideologically dismantle the pervasive epistemic fetishism undergirding health research and praxis.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".