Bibliographic record
Abstract
Abstract To represent the plurality of methods used within the Critical Studies Security community, this chapter surveys discourse analysis, corporeal analysis, ethnographic research, new materialism, and field analysis. Separating these practical methods from their ontological stakes makes critical analysis mutually intelligible and fosters a collobarative and plural discussion that shies away from doctrinaire orthodoxy. As a guide for analysis, this chapter also sets out some consensus positions about basic methods that are used in this field that critical scholars share and use in different theoretical traditions for their research design: idetifying standards of clarity, fit, and reflexivity by which critical scholarship can be judged, not on its ethical claims or its take on criticality, but rather on grounds of rigor.
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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 | 0.107 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".