The War at Home: Militarized and Racialized Identities in the University ‘Critical Language’ Classroom
Bibliographic record
Abstract
Despite the defunding and shuttering of many language courses and departments in public American universities, offerings deemed ‘critical’ to security and military interests have seen a dramatic rise since 11 September 2001. These courses are largely populated by Reserve Officers’ Training Corps (ROTC) learners interested in career advancement and payment through military stipends for course enrollment and ‘heritage’ learners interested in deepening their familial connections and cultural identities as expressed through language. Drawing on nine months of participant-observation and interviews in one such course, the author identifies three locally constructed symbolic boundaries ( us/them; soldier/civilian; white/non-white) used by students to reflect unequal identities and classroom experiences. Findings suggest that the federally-funded American critical language classroom can serve as a domestic stage upon which ROTC students may informally ‘try on’ militarized identities vis-à-vis classmates who are sartorially, spatially, culturally, and racially cast as native-civilian others.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.048 | 0.041 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| 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".