Bibliographic record
Abstract
Over the course of the early modern period, Europeans came to look at, engage with, and even transform nature and the environment in new ways, as they studied natural objects, painted landscapes, drew maps, built canals, cut down forests, and transferred species from one continent to another. The term “nature” meant many things during this period, from the inmost essence of something to those parts of the world that were nonhuman, such as the three famous “kingdoms” of nature: the animal, the vegetable, and the mineral. This article focuses on nature in this latter sense and broadens it out to include more recent understandings of the modern term “environment,” so as to encompass not only plants, animals, and rocks but also entire landscapes. Scholars from a wide variety of fields, ranging from the histories of science, art, and literature through historical geography, historical archeology, historical ecology, and landscape history, have long been interested in issues related to the environment and the natural world; more recently, they have been joined by practitioners of “environmental history” and additional branches of the environmental humanities and social sciences, who have drawn on these preexisting approaches and brought still further perspectives to the table.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".