Angel Patriots : the crash of United Flight 93 and the myth of America
Bibliographic record
Abstract
When United Flight 93, the fourth plane hijacked in the September 11, 2001 terrorist attacks, crashed into a field near Shanksville, Pennsylvania, the gash it left in the ground became a national site of mourning. The flight’s 40 passengers became a media obsession, and countless books, movies, and articles told the tale of their heroic fight to band together and sacrifice their lives to stop Flight 93 from becoming a weapon of terror. In Angel Patriots, Alexander Riley argues that by memorializing these individuals as patriots, we have woven them into much larger story of our nation—an existing web of narratives, values, dramatic frameworks, and cultural characters about what it means to be truly American. Riley examines the symbolic impact and role of the Flight 93 disaster in the nation’s collective consciousness, delving into the spontaneous memorial efforts that blossomed in Shanksville immediately after the news of the crash spread; the ad-hoc sites honoring the victims that in time emerged, such as a Parks Department-maintained memorial close to the crash site and a Flight 93 Chapel created by a local Catholic priest; and finally, the creation of an official, permanent crash monument in Shanksville like those built for past American wars. Riley also analyzes the cultural narratives that evolved in films and in books around the events on the day of the crash and the lives and deaths of its "angel patriot" passengers, uncovering how these representations of the event reflect the myth of the authentic American nation—one that Americans believed was gravely threatened in the September 11 attacks. A profound and thought-provoking study, Angel Patriots unveils how, in the wake of 9/11, America mourned much more than the loss of life
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".