Des modes de savoir pour affirmer une épistémologie disciplinaire infirmière : des surinterprétations et réductions dans les travaux de Chinn et Kramer
Bibliographic record
Abstract
Every academic discipline nurturing its own science draws on the development and dissemination of a knowledge. In pursuit of this project, the nursing discipline has proposed various conceptualizations of knowledge. The one developed by Chinn and Kramer, following Carper’s work, is without doubt the most prominent and most often borrowed concept in discussing the epistemological foundations of the discipline. This thoughtful article proposes an in-depth critical analysis of this theoretical development by focusing on “personal” and “esthetic” knowledge. As part of the analysis, the author scrutinizes the inherent logics that support these patterns of knowing. Finally, the author highlights some overinterpretations and discrepancies with regard to nursing practice.
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 | 0.037 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.018 |
| 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".