:<i>Fish into Wine: The Newfoundland Plantation in the Seventeenth Century</i>
Bibliographic record
Abstract
Combining innovative archaeological analysis with historical research, Peter E. Pope examines the way of life that developed in seventeenth-century Newfoundland, where settlement was sustained by seasonal migration to North America's oldest industry, the cod fishery. The unregulated English settlements that grew up around the exchange of fish for wine served the fishery by catering to nascent consumer demand. The English Shore became a hub of transatlantic trade, linking Newfoundland with the Chesapeake and New England, England, southern Europe, and the Atlantic islands. Pope gives special attention to Ferryland, the proprietary colony founded by Sir George Calvert, Lord Baltimore, in 1621, but later taken over by the London merchant Sir David Kirke and his remarkable family. The saga of the Kirkes provides a narrative line connecting social and economic developments on the English Shore with metropolitan merchants, proprietary rivalries, and French competition. Employing a rich variety of evidence to place the fisheries in the context of transatlantic commerce, Pope makes Newfoundland a fresh point of view for understanding the demographic, economic, and cultural history of the expanding North Atlantic world.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".