Bibliographic record
Abstract
It comes as no surprise that our interest in the past reflects our current preoccupations. This has been the case since Herodotus and Thucydides, both of whom turned to history to discover how to live in the present. Our concern with the clashes of civilizations and cultures, empires and states has been one of the few constants in historical research over the last two millennia. But fashions come and go, and our fascination with the origins of wars diminishes over time. More popular with “history buffs” are the wars themselves: the strategy of the generals, the tactics on the battlefields, the technological innovations. Outside of the historical profession there is little interest today in the origins of, say, the War of the Sicilian Vespers, the War of 1812, the Crimean War, or even the war in Vietnam. The exception is the continued interest in the causes of the First World War. The recent flood of books on the subject testifies not only to the celebration or commemoration of the 100th anniversary of its outbreak, but also to our frustrated need to make sense of the cataclysmic event that reshaped the modern world.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".