Surveying Medieval Perceptions of Nature Using a Combination of Historical and Scientific Sources
Bibliographic record
Abstract
In a modern world facing unprecedented anthropogenic environmental disaster, the academic interdisciplinarity is more relevant than ever. Environmental history is an area of study that requires the collaboration of historians and scientists alike. However, research is typically done from a one–sided perspective with little effort to understand the other. As many experts agree, geography heavily influences history.1 Environmental historians must use scientific proxy data regarding past ecological conditions in addition to a variety of historical sources, including religious texts, art, mythology, architecture, economics, and pieces of literature, to reconstruct past perception of nature. By doing so, the historian can gain a deeper insight into the scientific phenomena going on at the same time as historical events and movements to find potential connections. This can be seen in many different areas of environmental history research. This study analyzes various sources of medieval European animals, plants, water, and land use to gain an understanding of the diverse attitudes that people living during this time period held towards their environment. This ensures that environmental historians do not fall into the habit of resorting to approaches that make false generalizations, as leading figures in the field, such as Joyce E. Salisbury and Richard C. Hoffman, have criticized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".