Bibliographic record
Abstract
Nearly 20 years after the September 11, 2001 terrorist attacks on New York, Washington D.C., and Shanksville, PA there is a yearly ritual in a majority of US Schools. On the anniversary each year, teachers and students across the US learn about the attacks and memorialize the events. In many classrooms this is done through witnessing the events much like in 2001 for most of the world – through watching news or documentary footage of the events. In this article I use Hall’s concepts of encoding and decoding as well and socio-cultural theories to read these media representations both in the context of 2001 and again 20 years later to understand how these events are placed into broader narratives of US history. Many teachers today focus on the shock and horror of the events, an approach I argue is problematic as the affective response is emphasized over the historical context and consequences. Instead of using these media to foster collective memory, they could instead be viewed as primary sources to inquire into the historical context of the events and response in the form of the Global War on Terror. This approach would allow students to better understand the events leading to the attacks and the impact that the resulting responses by the US and other Western nations have had on their lives and the lives of others around the globe (e.g., Islamaphobia). After 20 years of conflict after these attacks it is time to both remember the victims of 9/11 as well as understand why it happened and the global toll of the response.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".