The Case For A Posthuman Responsibility to Protect: An Anishinaabeg Posthuman Security Convergence
Bibliographic record
Abstract
Critical Security Studies (CSS) seeks to shift the referent point of analysis away from the traditional frameworks employed by Traditional Security Studies (TSS), such as the primacy of the nation-state, guided by theories like realism and liberal-institutionalism. Nonetheless, while CSS aims to broaden the analytical lens beyond the state-centric view employed by TSS, it inadvertently falls into its own normative trap by overly centring its analysis on human-centric perspectives. This emphasis on anthropocentrism, while critiquing TSS for its narrow focus on the nation-state as the sole referent point of analysis, presents a paradoxical bias within CSS itself. The critique extends to the observation that CSS's human-centric bias not only narrows its analytical scope but also contributes to broader issues, particularly the exacerbation of the climate emergency. Therefore, this document advocates for the development of a 'posthuman security convergence' by integrating Anishinaabeg and European jurisprudences, as a form of "border thinking, or border epistemology." This framework aims to dismantle security studies’ anthropocentric referent point of analysis by merging the Anishinaabeg knowledge(s) of Chi-Naaknigewin (responsibility to the biosphere), with the existing Westphalian concept of 'responsibility to protect' (R2P) that is central to the dominant, intergovernmental security paradigm. Laying necessary, although not sufficient foundations for a prospective ‘posthuman responsibility to protect’ (PR2P), this document seeks to establish a new foundational basis for security studies that transcends its anthropocentric limitations, offering a topical and critical response to the pressing need for a security paradigm that encompasses environmental and ecological concerns in the face of the climate emergency.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".