Bibliographic record
Abstract
When I started this reader I was a tad suspicious that an environmental take on war would be an exercise in novelty related to the belated discovery of this generally ignored topic. In fact, this collection of essays adds to our theoretical and substantive understanding of how the environment and warfare interact. Most of this eleven-article collection deals with the impacts of war on the natural environment. The coverage is variegated, with topics ranging from war in precolonial central India, to precolonial and colonial African warfare, to the role of organisms in the battle of Gettysburg and the Civil War, to timber, pests, whaling, and broad environmental impacts of World War II on Japan and Finland. The introduction lays out the main themes clearly and is followed by a historical survey of the impact of war. (An irritant here is that the chapters are not numbered.) The key to an environment-war linkage is “to demonstrate that environmental approaches can yield valuable insights into fields of history that might not concede any potential connections at first glance” (p. 88). Most of the articles afford interesting insights and circuitous and sometimes surprising connections. Whereas images of battle-scarred landscape are commonplace in movies and sometimes photographs, the reach of war is far more extensive and often indirect. Economic and military mobilization (including new technologies), scarcity and attendant conservation activities, and diminishing trade can all have deep and persistent ecological consequences.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".