Bibliographic record
Abstract
In 1798, Sophie de Grouchy, the marquise de Condorcet, published a translation of the seventh edition of Adam Smith's Theory of Moral Sentiments (1792), along with a series of eight “letters” on the subject of sympathy. These letters are, in fact, substantial essays that allow us to discern how she read Smith. Intellectual historians have a tendency to privilege an author's intent, and to read the Theory of Moral Sentiments in order to determine what Smith actually meant, and how meaning was constructed in the context of a particular intellectual environment. As long ago as 1978, literary theorists such as Wolfgang Iser suggested that a reader's response is at least as interesting a question as an author's intent (Iser 1978). And Sophie de Grouchy is no ordinary reader. Her translation of, and commentary on, Smith's work allow us to see how a theory constructed in the intellectual context of the Scottish Enlightenment would be received by a different intellectual community. While de Grouchy shared much of the background that informed Smith's work, she could not write a commentary on sympathy during the Terror without taking into account recent French political experience and debate. And, I argue, her reading was not merely idiosyncratic, but rather representative of a particular group of intellectuals seized with the problem of adapting Enlightenment theory to the political reality of the Republic.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".