Risk's Refusal: Adam Smith on the Practices and Politics of Political Economy
Bibliographic record
Abstract
In Adam Smith’s work, we can find two intertwined narratives about risk: one that frames it as an opportunity for profit, and another that frames it as a threat to security. These are inseparable, as he argues that human beings have a risk-loving side that drives us to take great chances to pursue gain, but we also clamor to secure ourselves against possible loss, when the probability of success or failure is unknown. This essay draws on material in The Wealth of Nations and A Theory of Moral Sentiments to interpret Smith’s account of the search for security in risk-laden enterprises, with a focus on two approaches. First, Smith argues for the cultivation of trust and creditworthiness as a means of offsetting the necessarily limited knowledge that hounds international trade; Smith offer an account of the prudent merchant, whose observable practices and character cultivate a climate of trust that offsets the risks of trade. But, WN records instances of a more pathological approach, one involving the manipulation of systems to offset the perils of risk-taking. Smith notes corrupt approaches to the risks of trade, many of them political: collusion, manipulation of trade, and the insulation of irresponsible companies. The paper will consider what separates these two accounts, arguing that they illuminate Smith's epistemological claims about the limits of knowledge and the futility of large-scale planning.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".