Twelve Insights into the Afghanistan War through the Photographs from the Basetrack Project: Rita Leistner’s iProbes and Marshall McLuhan’s Theory of Media
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
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.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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