Bibliographic record
Abstract
The discipline of nursing has long maintained that is has a unique contribution to make within the health care arena. This assertion of uniqueness lies in great part in the discipline's claim to a distinct body of knowledge. Nursing knowledge is characterized by diverse and multiple forms of knowing and underpins the work of all nurses, regardless of field of practice. Unfortunately, it has been challenging for the discipline to take full ownership of its epistemological diversity, largely due to factors such as competing worldviews, and ideological and binary positioning. A philosophical middle ground stance is proposed as a way for the discipline to contemplate, discuss and develop nursing knowledge; a middle space that provides the freedom to consider competing worldviews while still allowing for the discipline to fully express itself in all of its epistemological diversity. In being able to enact its multiple forms of knowledge in a creative and open space that is open to different ideas and worldviews, not only can nursing take full ownership of its practice and its unique knowledge, it can also demonstrate how best to navigate an increasingly polarized world. In a world that is increasingly fixated on binary solutions and dualistic points of view, it is time for nursing to celebrate its epistemological diversity.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.020 | 0.067 |
| Scholarly communication | 0.039 | 0.043 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".