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.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".