Stance and strategy: post‐structural perspective and post‐colonial engagement to develop nursing knowledge
Bibliographic record
Abstract
How should nursing knowledge advance? This exploration contextualizes its evolution past and present. In addressing how it evolved in the past, a probable historical evolution of its development draws on the perspectives of Frank & Gills's World System Theory, Kuhn's treatise on Scientific Revolutions, and Foucault's notions of Discontinuities in scientific knowledge development. By describing plausible scenarios of how nursing knowledge evolved, I create a case for why nursing knowledge developers should adopt a post-structural stance in prioritizing their research agenda(s). Further, by adopting a post-structural stance, I create a case on how nurses can advance their disciplinary knowledge using an engaging post-colonial strategy. Given an interrupted history caused by influence(s) constraining nursing's knowledge development by power structures external, and internal, to nursing, knowledge development can evolve in the future by drawing on post-structural interpretation, and post-colonial strategy. The post-structural writings of Deleuze & Guattari's understanding of 'Nomadology' as a subtle means to resist being constrained by existing knowledge development structures, might be a useful stance to understanding the urgency of why nursing knowledge should advance addressing the structural influences on its development. Furthermore, Bhabha's post-colonial elucidation of 'Hybridity' as an equally discreet means to change the culture of those constraining structures is an appropriate strategy to enact how nursing knowledge developers can engage with existing power structures, and simultaneously influence that engagement. Taken together, 'post-structural stance' and 'post-colonial strategy' can refocus nursing scholarship to learn from its past, in order to develop relevant disciplinary knowledge in its future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".