Bibliographic record
Abstract
In the early sixteenth century, European mariners established a commercial fishery and site of permanent occupation in the northwest Atlantic. What are we to call this place? This article argues that mariners developed their own concept of space through the creation of the fishery, reflecting mental maps that evolved via the practice of fishwork. It contends that rather than modern terminology like “Newfoundland,” scholars should utilize this distinct geographic framework when discussing the early fishery and colonization. Mariners across Europe used variations of the term Terra Nova to label a malleable, vast, and watery world in the northwest Atlantic. Their usage was consistent across time and space, and tied geography to the act of fishing. The article reconstructs the nature of sixteenth-century mental maps, traces the origin and spread of the term Terra Nova, and considers how it differed from the geographies and labels of cartographers. In its final section, it reflects on the relationship between work, water, and space, and the ways this contributed to the use of Terra Nova. In so doing, it offers a way to recover lost mental maps and demonstrates the flexibility of maritime geographies in the early history of European expansion into the Atlantic.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".