Thinking through critical posthumanism: Nursing as political and affirmative becoming
Bibliographic record
Abstract
As a rejection and continuous reframing of theoretical humanism, critical posthumanism questions and imagines the human condition in the current context, aligning it with nonhuman and more than human entities, past and future. While this philosophical approach has been referenced in many academic disciplines since the 1990s, it has been gradually garnering interest among nursing scholars, leading to questions such as what it means to be human and what it means to be a nurse in the here and now. As a deeply ethical and political project, posthumanism, which we associate with poststructuralist concepts of power and resistance, questions the formation of posthuman subjects who more accurately reflect complex times, characterized by capitalistic commodification of life-human and nonhuman. In this article, we aim to explore how the ontological and epistemological underpinnings of critical posthumanism, specifically through Rosi Braidotti's works, can be useful to understand a posthuman subjectivity that favors affirmative actions aimed at actualizing our world in becoming. Through examples in nursing practice, education, and research, we will explore not only how critical posthumanism allows us to frame transformations in the current situation that we are embedded in as nurses and more generally as beings but also how these examples allow us to move beyond critique to the actualization of affirmative actions that correspond to the creation of new worlds.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.122 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".