Teaching the Post-September 11 Wars to the Post-September 11 Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do you teach the politics of the post-September 11 wars to the post-September 11 generation? Students passing through undergraduate programs in political science in the middle of the current decade were young children on September 11, and they have never known a world without the politics of the post-September 11 wars roiling in the background. In that sense, the post-September 11 wars have been an ordinary, perhaps even unexceptional, part of their emerging political consciousness. Now, as these students reach the undergraduate level, they are presented with an IR curriculum that is deeply inscribed with the effects of events that, for them, do not have the resonance of lived experience. What IR teachers should be cognizant of is that the further away a generation gets from the core events, often the less general knowledge can be presumed. In this research paper, I explain techniques used to teach the post-September 11 Wars while reflecting on the pedagogical challenges and surprising outcomes of teaching a course on this topic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it