Shoot & capture: media representions of US military operations in Somalia 1992-93 and Fallujah 2004
Bibliographic record
Abstract
Mass media images and narratives have an important role to play in the workings of international and domestic politics. Technological developments, particularly in the late nineteenth and twentieth centuries, have enabled a rapidly- growing 'economy' of images, information and means of communicating. These new commodities are circulated in an international sphere defined by shifting and unequal power relations. With this context in mind, this work undertakes an analysis of media representations of place and people in selected coverage of the Somalia intervention of 1992-1993 and the American sieges of Fallujah, Iraq in 2004, looking at both media narrative and imagery. Despite technological changes and differences in political context, coverage content for each case study illustrates many similarities in representations of places and people. Both case studies highlight the continuing resonance and use of long-standing racial and colonial stereotypes to describe, or to 'disappear', 'other' people and places. The aim of this project has been to recognise and problematize these powerful dichotomizations between a primarily Western 'us' and Others', illustrating the political nature of such attempts, their failings, and the consequences of these efforts at division. Exploration and exposure of the political nature of categorizations can assist in provoking a re-thinking not only of how 'others' are seen but of how 'we' construct our own identities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".