Harnessing the power to bridge different worlds: An introduction to posthumanism as a philosophical perspective for the discipline
Bibliographic record
Abstract
Although it is argued that social justice is a core concern for the discipline, nursing has not generally played a leadership role in the responses to many of the greatest social problems of our time. These include the accelerated rate of climate change, pandemic threats, systemic racism, growing health and social inequities, and the regulation of new technologies to ensure an equitable future 'for all.' In nursing codes of ethics, administration, education, policies, and practice, social justice is often claimed to be a core value, yet it is rarely contextualized by philosophical or theoretical underpinnings. It appears that nurses' commitment to social justice may stem more from a penchant for 'doing good' than an attempt to explore, understand, and enact what is meant by social justice from an ontological, epistemological, and methodological perspective. We contend that the dominance of a human science perspective in nursing contributes to a narrow definition of health and relegates many issues central to social justice to the margins of nurses' care. In this article, we explore how the focus on 'the human' in the human science perspective may not only be limiting the capacity of nurses to develop strategies to adequately address social injustice, but in some instances, direct nurses to contribute to their very reproduction. We suggest that a critical interrogation of this human-centric hegemony can identify avenues of rupture and introduce posthumanism as an additional philosophical perspective for consideration to help bridge the human-social divide.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| 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".