Institutionally Constrained Technology Adoption: Resolving the Longbow Puzzle
Bibliographic record
Abstract
For over a century the longbow reigned as undisputed king of medieval European missile weapons. Yet only England used the longbow as a mainstay in its military arsenal; France and Scotland clung to the technologically inferior crossbow. This longbow puzzle has perplexed historians for decades. We resolve it by developing a theory of institutionally constrained technology adoption. Unlike the crossbow, the longbow was cheap and easy to make and required rulers who adopted the weapon to train large numbers of citizens in its use. These features enabled usurping nobles whose rulers adopted the longbow to potentially organize effective rebellions against them. Rulers choosing between missile technologies thus confronted a trade-off with respect to internal and external security. England alone in late medieval Europe was sufficiently politically stable to allow its rulers the first-best technology option. In France and Scotland political instability prevailed, constraining rulers in these nations to the crossbow. The most important thing in the world, for battles, is the archers. (Philippe de Commynes, late medieval chronicler [quoted in Rogers 1993, p. 249])
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".