Medieval Sensibilities. A History of Emotions in the Middle Ages
Bibliographic record
Abstract
What do we know of the emotional life of the Middle Ages? Though a long-neglected subject, a myriad of sources - spiritual and secular literature, iconography, chronicles, as well as theological and medical works - provides clues to the central role emotions played in medieval society. In this work, historians Damien Boquet and Piroska Nagy delve into a rich variety of texts and images to reveal the many and nuanced experiences of emotion during the Middle Ages. From the demonstrative shame of a saint to a nobleman's fear of embarrassment, from friendship among monks to suffering in imitation of Christ, from the enthusiasm of a crusading band to the fear of a town threatened by the approach of war or plague, the examples are countless. Boquet and Nagy show how these outbursts of joy and pain, while universal expressions, must be understood within the specific context of medieval society. During the Middle Ages, a Christian model of affectivity was formed in the ‘laboratory’ of the monasteries, one which gradually seeped into wider society, interacting with the sensibilities of courtly culture and other forms of expression. Bouqet and Nagy bring a thousand years of history to life, demonstrating how the study of emotions in medieval society can also enable us to understand better our own social outlooks and customs. Medieval Sensibilities will be of great interest to students and scholars of the Middle Ages, as well as to general readers interested in new perspectives on the past.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".