Adaptation of The Lord of the Rings in War on Terror Era
Bibliographic record
Abstract
This paper looks at the success and popularity of Jackson’s adaptation of The Lord of the Rings in light of its context of reception. The Lord of the Rings had to wait for more than forty years since its first publication in 1954 only to enter Hollywood after the 9/11 attacks when a global War on Terror was declared with Bush’s famous statement “you’re either with us, or against us”. During the past few years there has been a growing tendency towards fantasy films mostly adapted from novels and other literary forms. Harry Potter , Twilight , Spiderman , The Hobbit and The Vampire Diaries are only some out of many examples. Knowing this, it is now a concern of scholars of Literature and Cinema alike to study the ins and outs of this newly formed trend. Using the theories of Adaptation hand in hand with media-cultural studies, this paper means to argue that The Lord of the Rings owes much of its fame and success to its context of reception. The film resonates with many contemporary concerns of the post 9/11 era such as the issue of ‘vulnerable boundaries’, ‘faceless “Other” terrorist’, ‘Good vs. Evil’, and the destructive force of war over power in general.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".