Canadian Entrepreneurs and the Preservation of the Capitalist Peace in the North Atlantic Triangle in the Civil War Era, 1861–1871
Bibliographic record
Abstract
In their 2013 bookReimagining Business History, Philip Scranton and Patrick Fridenson called on business historians to reassess militarization and the “two-way exchanges” between the military and the private sector. The call is timely. The extensive business-historical scholarship on the relationship between companies and war sensibly focuses on companies that profited from their involvement in the military-industrial complex.1The business-historical literature is virtually silent, however, on the role of business in preventing wars from starting in the first place. In other words, business historians have missed a productive opportunity to engage with capitalist peace theory (CPT), an increasingly important theory in the discipline of international relations (IR). Many IR scholars now argue that the mutual economic interdependence characteristic of global capitalism reduces the likelihood of war. Their research suggests that while extensive cross-border economic linkages do not preclude the possibility of war, the creation of a transnational community of economic interests tends,ceteris paribus, to reduce the frequency, duration, and intensity of warfare.2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".