Bibliographic record
Abstract
It is only fair to ask: Why another book on 1914? Surely, the origins of that war have been studied, reviewed, and revised almost beyond any reader's endurance. Vladimir Dedijer, arguably the leading expert on the Sarajevo assassination, claimed that already in 1966 more than 3,000 books had been published on that subject alone. And the torrent of ink spilled on that tragic murder has never abated. Hence, why more? The short answer is that many of us have missed several key elements in the vast literature on 1914. First, who precisely were the decision makers? Monarchs, presidents, foreign ministers, staff chiefs, or a combination of these? And what were their mindsets in July 1914? How had the experiences of the recent past (and especially of the two Balkan Wars of 1912–13) shaped their outlooks? Second, how did those governments go about declaring war? In other words, was there a constitutional definition of war powers? Were cabinet and parliamentary approval required in all cases? Or could war be declared simply by royal fiat? Third, which “social forces” or extraparliamentary lobbies had input into the decision for war? And fourth, what were the reasons? What were the justifications for the decisions to go to war? Why did those decision makers do it? Were there common or similar justifications? Or is a differentiated reading needed? In short, we sought answers to questions that had troubled us from previous readings on July 1914.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".