The RAD Synthesis: Taxonomy and Decolonial Ethic of Emerging Digital Technologies for Nursing Informatics
Bibliographic record
Abstract
This paper is a response to Arries' call for exploration of a universal nursing ethics in the context of a pluralistic world, specifically consideration of an African ethics for nursing holding Ubuntu as a community-centred humanistic ideal with the "…ability to complement, extend and synthesize other ethical frameworks in nursing practice" [1]. Our discourse synthesizes the Arries-Davies taxonomy of ethical challenges of emerging digital technologies (EDTs) in healthcare [2] with sequential applications of Kwete et al.'s [3] colonial remnants framework and of Ubuntu [1][3][4][5]. This sequential synthesis of cognitive tools for identification, classification, and analysis of EDT ethical issues through a decolonial lens is dubbed the Rose-Arries-Davies (RAD) Synthesis. Key concepts explored include description of the Arries-Davies Taxonomy, introduction to the concept of colonial remnants, an introduction to Ubuntu, and demonstration of the RAD Synthesis to engage in decolonial critiques of global health informatics.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".