Bibliographic record
Abstract
Cundill History Prize Finalist Longman– History Today Prize Finalist "Meticulous environmental-historical detective work." — Times Literary Supplement When Europeans first arrived in North America, they faced a cold new world. The average global temperature had dropped to lows unseen in millennia. The effects of this climactic upheaval were stark and unpredictable: blizzards and deep freezes, droughts and famines, winters in which everything froze, even the Rio Grande. A Cold Welcome tells the story of this crucial period, taking us from Europe's earliest expeditions in unfamiliar landscapes to the perilous first winters in Quebec and Jamestown. As we confront our own uncertain future, it offers a powerful reminder of the unexpected risks of an unpredictable climate. "A remarkable journey through the complex impacts of the Little Ice Age on Colonial North America…This beautifully written, important book leaves us in no doubt that we ignore the chronicle of past climate change at our peril. I found it hard to put down." —Brian Fagan, author of The Little Ice Age "Deeply researched and exciting…His fresh account of the climatic forces shaping the colonization of North America differs significantly from long-standing interpretations of those early calamities." — New York Review of Books
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.300 | 0.143 |
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".