Complex relationships : the state, privateers, & organized crime
Bibliographic record
Abstract
The purpose of this research is to examine the history and nature of privateers during the 17th through the 19th centuries with the aim in answering this question: Using contemporary definitions, can the business of privateering can be categorized as organized crime?Privateering has long been considered, not only a legal course of reprisal for wartime losses, but also a heroic action that was celebrated, at least on the side of the privateer.The reality is more complex.In order to explain why privateers were employed despite the harm they perpetrated throughout the Maritimes during the seventeenth to nineteenth centuries, this paper incorporates a blend of sociology, criminology, Atlantic Canadian history, and political economy to show the connection between privateering and organized crime.It draws on a combination of sources to gather data on the complex history and nature of privateering.It also applies a combination of definitions to show that this class of mercenary/merchant marine were not only necessary in establishing the interests of foreign powers in Canada, but were also instrumental in the foundation and development of the early government in the Maritimes; shaping the course Canada would rise to or take in the coming two centuries.Finally, Stephen Schneider's 23-point comprehensive taxonomy of the characteristics of an organized crime conspiracy is applied, along with the historical and contemporary evidence to point to a classification of privateering as organized crime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".