Bibliographic record
Abstract
The discovery of America and, more broadly, the European expansion to other continents are the major events characterizing the trade networks of the Renaissance. Several scholars have discussed the impact of these factors on European development as well as on the world’s steps toward capitalism and globalization. Circa the mid-17th century (the chronological limit of this bibliography), however, inter-European trade still made up the majority of overall trade. By and large, trade was not badly affected by the otherwise disastrous consequences of the Black Death of the mid-14th century. The demands of those who survived, constantly fueled by a wider range of products available on the market, along with a much-improved transport system, led to an increase in the volume of trade. International merchants were able to set up extensive commercial networks or broaden existing ones, which extended into a number of prominent towns. Beginning in the 16th century, following the exploration of the African coast by the Portuguese, their arrival in India, and, in particular, the discovery of America, trade expanded globally. Commercial empires sprang up—first in the countries of the Iberian Peninsula, then in the northwestern European countries (notably England and Holland and, to a lesser extent, France). In the seventeenth century, merchants from these areas began to strengthen their influence in the Mediterranean, thus reversing what formerly had been the scenario in the late Middle Ages, when southern European and German merchants dominated in the North Sea.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.121 | 0.032 |
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".