‘How dare she?!’: Parrhesiastic resistance and the logics of protection of/in international security
Bibliographic record
Abstract
Abstract Malalai Joya, Greta Thunberg, Idle No More leaders – what do these figures have in common? They each decided to act/speak out against the failures, lacks, exclusions, violence and injustices in the words and deeds of different authorities claiming to act on behalf of (their) security and protection, and thus made visible, challenged and disrupted the dominant logics of protection on which such claim is based. More specifically, they each enacted this critique by performing a contemporary form of parrhesia – a practice in Ancient Greece that consisted in speaking truth frankly and courageously to power, taking risks in doing so out of a sense of duty to improve a situation for oneself and others. Yet none of these women stated anything radically new or shockingly unknown. So why, then, did speaking truths that were already known lead to such dire consequences and intense reactions? This article will argue that by mobilizing the frameworks of logics of protection and parrhesia together, we can have a fuller understanding of these figures’ dissident truth-speaking: it is precisely their positionings within logics of protection that made their truths so daring and, in turn, it is through parrhesia that Joya, Thunberg and Idle No More activists made logics of protection visible through their disruption, opening up potentialities for ‘doing’ and ‘being’ otherwise. The dual framework offered in this article thus offers interesting avenues through which to explore resistance, truth and protection in (feminist) security studies today.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.086 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".