Twelve Insights into the Afghanistan War through the Photographs from the Basetrack Project: Rita Leistner’s iProbes and Marshall McLuhan’s Theory of Media
Bibliographic record
Abstract
This article presents the iProbe concept developed by the Canadian photographer Rita Leistner. This analytical tool is one of the ways to present the image of modern warfare that emerges from messages in social media and photographs taken using smartphones. Utilized to understand the approach are photographs Leistner took at the American military base in Musa Qala (Helmand province, Afghanistan) during the implementation of the “Basetrack” media project in 2011. The theoretical basis for this study is Marshall McLuhan’s media theory, which was used by the photographer to interpret her works from Afghanistan. Leistner is the first to apply the various concepts shaped by McLuhan in the second half of 20th century, such as “probe”, “extension of man”, and the “figure/ground” dichotomy, to analyze war photography. Her blog and book entitled Looking for Marshall McLuhan in Afghanistan shows the potential of using McLuhan’s concepts to interpret the image of modern warfare presented in the contemporary media. The application of McLuhan’s theory to this type of photographic analysis provides the opportunity to focus on the technological dimension of modern war and to look at warfare from a technical perspective such as what devices and communication solutions are used to solve armed conflicts as efficiently and bloodlessly as possible. Therefore, this article briefly presents twelve iProbes that Leistner created based on her experiences from working in Afghanistan concerning photography, military equipment, interpersonal relations, and various types of communication.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.032 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".