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Record W3035513269 · doi:10.1111/nup.12309

Is feyerabendian philosophy relevant for scientific knowledge development in nursing?

2020· article· en· W3035513269 on OpenAlexaff
Marie‐Lee Yous, Patricia H. Strachan, Jenny Ploeg

Bibliographic record

VenueNursing Philosophy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsPhilosophy of sciencePluralism (philosophy)CreativityEpistemologyRelevance (law)Sociology of scientific knowledgeEngineering ethicsSociologyNursing scienceNursingPsychologyPhilosophyMedicinePolitical science

Abstract

fetched live from OpenAlex

To revitalize nursing science, there is a need for a new approach to guide nurse scientists in addressing complex problems in health care. By applying theoretical concepts from a revolutionary philosopher of science, Paul K. Feyerabend, new nursing knowledge can be produced using creativity and pluralistic approaches. Feyerabend proposed that methods within and outside of science can produce knowledge. Despite the recognition of Feyerabendian philosophy within science, there is currently a lack of literature regarding the relevance of Feyerabendian philosophy for nursing science. We aim to (a) describe and critique Feyerabendian concepts, (b) discuss the potential application of Feyerabendian philosophy for knowledge production within gerontological nursing and (c) describe theoretical possibilities for nurse scientists in using Feyerabendian philosophy to guide nursing knowledge development. We begin by introducing Feyerabend's life and his inspirations for his theoretical concepts, epistemological anarchism, theoretical pluralism and humanitarianism, and conclude by offering suggestions of how to apply Feyerabendian philosophy in nursing research.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.223
GPT teacher head0.499
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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