Bibliographic record
Abstract
In this article, the author recounts some of the events that occurred on September 11, 2001, when four doomed airlines crashed after being hijacked by 19 Al-Qaeda terrorists, resulting in the deaths of 2,977 people in New York, New York, at the Pentagon in Arlington, Virginia, and on an empty field in Shanksville, Pennsylvania. It is at this latter location, where United Flight 93 crashed killing everyone onboard, including 31-year-old Mark Bingham, an openly gay businessman and member of a small group of people who, it is believed, wrested control from the hijackers and brought the plane down. In the years post-September 11, Bingham has become known as a modern-day hero by the various queer communities, while also garnering a high level of notoriety among many mainstream people as well. The author maintains, however, that Bingham’s hero status simultaneously contributes to the dismissal and erasure of countless other queer people, primarily Black, Brown, and transgender, who have also performed heroic acts throughout modern U.S. history. Without diminishing the actions Bingham and the others took on board United Flight 93, the author questions why this particular gay man is remembered, while countless other queer/trans people of color remain largely unknown.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.024 |
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".