Bibliographic record
Abstract
In the last few years there has been a tremendous surge in research projects focusing on the history of emotions. Historians all over the world, from Australia to London, from Princeton to Madrid, from Canada to Paris, have started to examine emotions from a historical perspective. Among the many individual and collective projects, the Berlin Center for the History of Emotions holds a special place. Since its founding in 2008, a group of twenty to thirty historians have devoted their research efforts to the single but complex goal of historicizing emotions. As an integral part of the Max Planck Institute for Human Development, the center is sufficiently funded to carry out such basic research and will continue to do so for years. It offers superb working conditions, providing offices and excellent library resources to its pre- and postdoctoral fellows and organizing weekly seminars and a great number of international conferences with the participation of distinguished scholars. Furthermore, the center welcomes visiting researchers (who mostly bring their own funding) and invites them to actively participate in and contribute to ongoing debates and events. Together with three major Berlin universities (Free University, Humboldt University, Technical University), the center launched an International Max Planck Research School for graduate training. Every year, six graduate students are accepted to the program, which focuses on moral economies of modern societies, with an emphasis on moral emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".