The Love-Hate Relationship with Experts in the Early Modern Atlantic
Bibliographic record
Abstract
As England belatedly moved into Atlantic enterprises, novel expertise was required. England’s first ventures across the ocean were in the fishing trade in Newfoundland, and this was a field they knew well. More southern regions beckoned, however, because these were expected to yield rich commodities. As they were drawn to these new areas, English undertakers found that a range of new skills was required, and they had to turn to foreigners or English people with foreign experience to get the expertise they needed. Everything from navigating in unfamiliar waters to building fortifications to growing novel crops meant reliance on experts. Colonists and their backers in England recognized the need but they hated such reliance, particularly because they often suspected that the so-called experts were bogus. Colonists believed that the experts—even when their skills were genuine—distorted life in the settlements by their demands and their focus. Part of the reason experts were distrusted was that their experiences gave them a cosmopolitan outlook, including sometimes a capacity to understand outsiders’ viewpoints. One goal of all early colonies was to achieve sufficient competence that they could eliminate the experts and manage their own enterprises.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".