History by generations : Generational dynamics in modern history
Bibliographic record
Abstract
Conference at the GHI Washington, December 9–11, 2010. Co-sponsored by the GHI and the graduate program “Generations in Modern History,” University of Gottingen. Conveners: Hartmut Berghoff (GHI), Bernd Weisbrod (Gottingen), Uff a Jensen (Max-Planck-Institut fur Bildungsforschung, Berlin), Christina Lubinski (GHI/Harvard). Participants: Astrid Baerwolf (Gottingen), Volker Benkert (Arizona State), Olof Brunninge (Jonkoping International Business School), Elwood Carlson (Florida State), Sarah E. Chinn (Hunter College, CUNY), Karl H. Fussl (Technical University of Berlin), Gary Cross (Pennsylvania State), Kirsten Gerland (Gottingen), Hope M. Harrison (George Washington University), Jochen Hung (Institute of Germanic & Romance Studies, London), Jan Logemann (GHI), Ondrej Matejka (Institute of Contemporary History, Prague), Daniel Morat (Free University of Berlin), Maria Fernandez Moya (University of Barcelona), Lutz Niethammer (University of Jena), Miriam Rurup (GHI), Dirk Schumann (Gottingen), Judith Szapor (McGill University), Anna von der Goltz (Cambridge), and several other members of the Gottingen graduate program “Generations in Modern History.” Johanna Brumberg (Gottingen), Uff a Jensen, and Georg Kamphausen (Bayreuth) submitted papers but were unable to attend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".