Appetite for Discovery: Sense and Sentiment in the Early Modern World
Bibliographic record
Abstract
Lucien Febvre’s 1941 call for historians to recover the histoire des sentiments is now routinely evoked by scholars in the wake of the recent “emotional turn” in the historical discipline. Historians would regain their “appetite for discovery” (goût à l’exploration) once they delved into the deepest recesses of the discipline, where history meets psychology, Febvre predicted. His plea followed the aims of a generation of scholars working in the early twentieth century—Johan Huizinga and Norbert Elias among them—who sought to recapture the affective lives of the past. Yet the history of sense and sentiment perhaps owes its greatest debt to Febvre and his colleagues in the Annales School, who, via the study of mentalités and private life, made the study of emotions a serious object of historical inquiry. Some four decades passed before Febvre’s challenge was taken up with any rigor. In the 1980s, the work of Peter and Carol Z. Stearns sought to chart the emotional standards and co des of past societies—something they termed “emotionology.” Since then, over the past three decades the history of emotions has been pioneered by scholars such as Barbara H. Rosenwein and William Reddy in seminal works that introduced us to now classic interpretative frameworks such as “emotional communities” and “emotives.” This burgeoning of interest in the history of emotions has now also found expression in a number of institutional research centers and publication series devoted to the subject.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".