“The Sum of All Fears” from Novel to Film: Shifting the Discourse of Terrorism
Bibliographic record
Abstract
This article aims at exploring the terrorist representation in the film The Sum of All Fears (2002), which is adapted from the novel with the same title. The study is drawn from Fairclough’s Critical Discourse Analysis, with its three dimensions of analysis. The first dimension is the micro level that deals with the language used in social practice. The second dimension analyzes the discourse practice, such as intertextuality, text production, and consumption, that relate to the reference of ideas presented. The third dimension, called the macro level, deals with the social context of a text, such as the practice of exercising power through particular discourse. The finding reveals that the adapted film directed by Phil Alden Robinson represents the terrorists whose identity differs from the one in the novel. Besides, the discourse of terrorism developed in the film has three essential elements, the nuclear weapon, the terrorist, and the international relation between America-Russia in danger. The novel and film share the same idea of nuclear weapons as the threat. The other similar aspect is the implication of a nuclear bomb attack on the international relations between the USA and Russia, which might lead to war. However, the terrorist identity in both media is shifted. In the novel, the terrorist is depicted as an Arab-nationalists, while in the film, the terrorist is a neo-Nazi. Both portrayals of terrorists involve stereotyping and labeling and represent different political discourses. The discourse of terrorism in the novel is represented as the Arab-origin terrorist. At the same time, the film underpinned the idea that terrorism implies the contestation of the Cold War or two superpower nations. The film also reveals that the individual breakthrough done by Jack Ryan proves the solution to the rigid bureaucracy at the top executive level.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".