Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it