Bibliographic record
Abstract
This essay argues that media systems are not passive instruments of military power but active infrastructures that shape how war is conceived, executed, and sustained. Building on Harold Innis’s staples theory and the materialist traditions in media scholarship, we analyze three case studies—the U.S. Civil War, World War I, and the ongoing conflict over rare-earth minerals in the Democratic Republic of Congo—to demonstrate how media-specific demands produce new regimes of logistics, extraction, and violence. In each historical moment, war is not simply conducted through media but organized around it: paper shortages in the 1860s tied to cotton blockades redefined print media as both resource and battleground; telegraphic entanglements during WWI transformed cable infrastructure into a target and tactical medium; and today’s digital economies sustain conflict through their dependence on minerals sourced from war-torn regions. Rather than treating media as ancillary to strategy, we position them as infrastructural cores of military operations. Media circuits demand raw materials, labor infrastructures, and spatial control—linking sovereign power to media logistics in enduring ways. Our analysis reveals that war and media are co-constitutive processes tied together by shared material conditions. From the newspaper to the fiber-optic cable, the terrain of conflict shifts in step with the demands of media technologies. This entwinement renders modern war a struggle not just over territory or ideology but over the infrastructures that make communication—and domination—possible.
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 | 0.000 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".