LAS EMOCIONES MEDIEVALES: EL AMOR, EL MIEDO Y LA MUERTE MEDIEVAL EMOTIONS: LOVE, FEAR AND DEATH
Bibliographic record
Abstract
RÉSUMÉ L’article propose de discuter une question épistémologique classique en histoire des émotions, aux implications méthodologiques importantes. L’historien doit-il chercher à découvrir l’expérience de l’émotion au-delà de ce qu’offrent leurs sources, comme de l’autre côté du miroir, ou sinon, doit-il abandonner l’espoir de faire une histoire de l’émotion? À partir de l’exemple des expériences affectives d’une mystique cistercienne, Lukarde d’Oberweimar (morte en 1309), telles que sa vita les présente, il s’agit dans un deuxième temps de proposer une approche de l’émotion et de l’expérience médiévales profondément ancrée dans leur culture anthropologique et religieuse propre –bien loin de l’émotion psychologique moderne–. MOTS CLÉS : émotion, expérience, sainteté, dévotion, femmes mystiques ABSTRACT This paper discusses a classical epistemological question in the history of emotions, which has important methodological implications for the practice. Do the historians have to look for an experience of emotion beyond what the sources offer to read, like through the looking glass? If not, do we have to abandon all hope to write a history of emotions? After the theoretical discussion, a second part of the article, drawing on the vita of the Cistercian mystic woman, Lukardis of Oberweimar (d. 1309), proposes to approach medieval emotion and experience as deeply embedded in their religious and anthropological culture, far away from the modern vision of emotion linked to psychology. KEY WORDS: emotion, experience, holiness, devotion, female mystics
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".