Adam Smith and French Political Economy: Parallels and Differences
Bibliographic record
Abstract
As is well known, Adam Smith spent about two years in Europe, most of it in France. It was in fact during his stay in Toulouse that he began to work on what became The Wealth of Nations ( WN ); 1 but what proved decisive for the deepening of his understanding of market processes were his encounters in Paris with Paul-Henri Thiry (Baron d’Holbach), Claude Helvetius, Jean d’Alembert, André Morellet, Jacques Necker, and especially his discussions with Anne-Robert-Jacques Turgot and Franç;ois Quesnay. 2 (Quesnay was universally regarded as the leader of the so-called Physiocrats, who also included Pierre-Paul Le Mercier de la Rivière, Pierre Samuel du Pont de Nemours, and Turgot — but the latter did not rigidly subscribe to the core dogmas of that school.) Although no one denies that Smith was profoundly influenced by these encounters, the question of precisely what debt Smith owed to these thinkers is not central to my purpose here. It is, indeed, a controversial one. Roberts (1935), for example, argued that Smith drew heavily from the writings of Pierre Le Pesant de Boisguilbert whom he would have known through later writers; Du Pont de Nemours and the Marquis de Condorcet, on the other hand, suggested that anything of value in Smith’s WN could be found in what Turgot had written (Groenewegen, 1968, p. 271). But this question is probably impossible to answer categorically, partly because Smith’s manuscript notes were destroyed after his death. To talk about an intellectual debt is to put the matter in terms that are too narrow and could be of interest only to erudite biographers. 3 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".