Garbage In, Garbage Out: Challenges of Model Building in Global History, A Military Historical Perspective
Bibliographic record
Abstract
This paper examines two prominent recent attempts to explain the phenomenon of the “rise of the West,” Ian Morris’s model of “Social Development” and Philipp Hoffman’s model of military power (Morris 2010, Morris 2013, Hoffman 2012, Hoffman 2015). Whereas most recent scholarship on the rise of the West has focused on economics, Morris and Hoffman widen the scope of comparison to other areas, in particular focusing on the measurement and explanation of divergences in military effectiveness. By drawing on recent work in China’s military history, the author shows that both models – but particularly that of Morris – are inadequate, falling back on older narratives of Western military superiority that have been challenged or disproven by recent scholarship in global military history. The article suggests, however, that the two models – and especially that of Hoffman – do raise significant new questions for future research, and it concludes by noting that what social scientists need more than new models at present is a closer attention to the rapid and ever increasing proliferation of scholarship in non-Western countries, and in particular that of the Sinophone world.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".