Chelsea Manning, national security, and the cishetero/homonormative logics of protection
Bibliographic record
Abstract
Abstract ‘I feel like a monster’, typed Chelsea Manning, referring partly to her gender identity but mostly to her job in the US military. Morally conflicted by what she saw and read while serving in Iraq, extremely isolated from her unit and experiencing emotional distress in relation to her gender identity, Manning would act on these stressors by leaking hundreds of documents to Wikileaks, and coming out as a (trans) woman. While she was quick to be classified as either a hero or a traitor, her case evades such dichotomisation and calls for more sophisticated readings. While a lot has been written on Manning in queer and transgender studies, surprisingly little has been published on this case in International Relations, not even in the quickly growing field of Queer IR. Yet Manning’s case helps highlight many of its core concerns in relation to issues of power, security, and sovereignty. In fact, what is often lost when reading the Manning case are the queer and trans logics of protection that were disrupted by Manning’s disclosures and that made such disruption possible. These dominant logics rely upon a culture of secrecy that must be preserved for performances of national security to hold true.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".