New evidence on the causes of the fluctuations in ocean freight rates in the 1850s: harvest failures, business cycles, and the Crimean War
Bibliographic record
Abstract
This paper takes a critical look at the hypothesis that the Crimean War was the main cause\nof the surge in ocean freight rates in the first part of the 1850s. The analysis is based on newly\nconstructed monthly data on world freight rates in the 1850s. A new type of freight rate index,\nreferred to as a repeat sailings index, is presented, which is similar to a type of index frequently\napplied to house prices. An econometric model of the determination of freight rates is estimated\non monthly data from the 1850s, from which it is possible to disentangle the effects on freight rates\ndue to the various demand and supply factors, including the Crimean War. It is found that harvest\nfailures, business cycles, the supply of tonnage and the Crimean War all significantly affected freight\nrates in this period. The Crimean War may have accounted for a quarter of the surge in freight\nrates in the years prior to the outbreak of the War; once the War broke out in March 1854, however,\nit was of less importance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".