Bibliographic record
Abstract
In April 1917 the United States entered World War I on the side of the Allies. The decision-making process and the grounds for entry were very different from those reviewed in previous chapters. There has been, understandably, much contention about the causes and significance of this action. Most contenders have been Americans – historians, journalists, popular writers, and, occasionally, politicians. The contention was most intense, curiously, in the 1930s, as Americans again saw the breakdown of international order and the approach of another major war. Since that time, the argument has largely subsided and has usually involved only a few historians. Evaluation has been one central concern in these discussions – whether intervention was a good or a bad thing? Did it bode well or ill for the future of the United States and the world? Many of these accounts made worthwhile contributions to our knowledge of the event, although many were highly colored with emotion and value judgments. A frequent element in those discussions was denigration of the role of individual actors. Partisans on both sides of the debate over the wisdom and morality of intervention have argued instead that some “great forces” of history – geopolitics, economics, and/or culture – largely determined what happened. With rare exceptions, these interpreters viewed individual decision-makers as witting or unwitting, honest or devious agents of great forces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.039 |
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".