Chaucer’s Gifts: Exchange and Value in the Canterbury Tales. By Robert Epstein
Bibliographic record
Abstract
Readers of Chaucer have long recognized the Canterbury Tales as deeply embedded in the burgeoning commercial environment of the later Middle Ages. Influential analyses by Lee Patterson, R. A. Shoaf, Lianna Farber, and others have revealed how the sprawling poem alternately naturalizes, parodies, embraces, and satirizes the economic and mercantile worlds that Chaucer – son of a wine merchant, long-time controller of the London wool custom, and clerk of the King’s Works – both inhabited and navigated. In Chaucer’s Gifts, Robert Epstein contributes to this critical conversation by reading the Canterbury Tales not strictly through the fourteenth-century mercantile economy but through an adjacent (and occasionally overlapping) gift economy, one structured by an ingrained ethos of reciprocity that functioned in opposition to an incipient, profit-driven capitalism. Drawing from twentieth- and twenty-first-century developments in economic anthropology, as well as from the gift theories proposed by Jacques Derrida and Pierre Bourdieu (both of whom Epstein productively resists), Chaucer’s Gifts illuminates an overlooked aspect of the Canterbury Tales. Epstein offers a nuanced reading of the exchange systems implicit in the poem, revealing in the process how Chaucer articulates ‘the potential for different types of socially generated value than those presumed by market transactions’ (p. 13).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".