Kasulis’ intimacy/integrity heuristic and epistemological pluralism in nursing
Bibliographic record
Abstract
Epistemological pluralism is a recognized feature of nursing knowledge, which embraces both objective, scientific knowledge and situated knowledge that include subjective experience, values and affect, and is encountered in relationship. While there is a lively literature about describing and validating the need for pluralism in nursing's knowledge base, there has been less discussion of how to work with and across different kinds of knowing that are used in practice. In this paper, I describe Kasulis' heuristic framework for understanding more clearly what is entailed in different kinds of knowledge, and what some of their advantages and disadvantages might be. The framework was created by Thomas Kasulis, an American scholar of Japanese philosophy who identified broad orientations in Asian and Western philosophies that he characterized as 'intimacy' and 'integrity', respectively. Kasulis emphasized that his framework is a heuristic, a tool for making distinctions more clearly between different styles of thinking, that can manifest not only between cultural traditions from different parts of the world, but also between subcultures within one of the dominant orientations. He breaks his two orientations down by five distinguishing categories of objectivity, relating, affect, embodiment and transparency. In this paper, each category is described and discussed in relation to aspects of nursing knowledge. Looking at different epistemological viewpoints in this way helps to clarify their differences, and to explain the difficulty of reading across them, when they entail basic assumptions that are not commensurable with each other. Kasulis' framework offers a new way of reading across viewpoints commonly seen in the epistemological pluralism of nursing. It is a tool that can sharpen critical discernment about what is at stake, what can be gained, and what might get missed while operating in either the intimacy or integrity orientation.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".