Bibliographic record
Abstract
The Embargo acts, passed in 1806-1808 during the Jefferson administration, were originally designed to punish Great Britain for violating American neutrality on the high seas during the Napoleonic wars. Increasingly, however, the acts were enforced against Americans seeking to defy the embargo and trade with England. Since Maine was heavily committed to trading with Great Britain — and with its colonies immediately to the north of Maine — the War Department ordered several forts built along the District’s coast, ostensibly to protect American citizens from British reprisal or war, but in fact, to enforce the embargoes. The forts brought sharply divided feelings in Maine. Joshua Smith is assistant professor of history at the U.S. Merchant Marine Academy and a specialist in maritime history, naval history, New England history, and Canadian-American relations. He received his M.S. in maritime history and underwater archaeology from East Carolina University, an A.S. from Maine Maritime Academy, an M.A. from the University of St. Andrews in Scotland, in his PhD from the University of Maine. He published Borderland Smuggling: Illicit Trade and the Formation of the U.S.-Canadian Border, 1783-1820 in 2005, and has published several articles in maritime and naval history and a two-volume edited series of documents in American Maritime History.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".