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COHERENCE, CREATIVITY AND AUDACITY IN METHODOLOGICAL DECISIONS

2023· article· en· W4367665530 on OpenAlexaff
Margareth Santos Zanchetta, Kateryna Metersky

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCreativityCoherence (philosophical gambling strategy)PsychologySociologySocial psychologyPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Carper's ways of knowing (e.g., empirics, aesthetics, ethics, and personal) undeniably imprinted the generation and understanding of nursing knowledge 1 positioning nursing as a standalone profession separate from medicine.While Chinn and Kramer 2 have added to these ways of knowing to include emancipatory knowing, today, there are calls 1,3 for inclusion of the political way of knowing to this list to reshape nursing research methodologies to generate socially relevant knowledge that can respond to claims for social justice and health equity.Noncolonialist philosophies, advocacy actions for human rights, and anti-discriminatory policy development genuinely should guide nursing research targeting the social determinants of health 1 .Decolonization of nursing knowledge legitimates noncolonial philosophies promoting the goals of social justice and humanization for all 3 .To achieve this, research-practice-education agendas need to include diverse individuals' frames of reference, knowledge patterns, and culture 3 .This will best inform policy reviews and development.To counteract the imprints of colonialist philosophies over the South epistemology 4-5 , Santos proposed the paradigm of prudent knowledge for a decent life 5 Its principles refer to scientific-natural knowledge in its forms as social, local, and common sense.Therefore, redesigning methods to mobilize all social, technological, and instrumental assets 3 to increase recruitment and participation of hardto-reach populations (e. g., living in distant locations, at risk for social isolation, limited or diminished exposure to research) using technology, demonstrates audacity and freedom to integrate an extensive number of approaches and methods 6 .For instance, human-centered design incorporates overlapping collaborative processes and data collection procedures in eHealth projects [7][8] .Another example is the

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 imitation

Not 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.

metaresearch head score (Codex)0.621
metaresearch head score (Gemma)0.665
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.621
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6210.665
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0140.006
Science and technology studies0.0100.126
Scholarly communication0.0320.034
Open science0.0120.027
Research integrity0.0130.022
Insufficient payload (model declined to judge)0.0040.002

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 head0.916
GPT teacher head0.628
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations6
Published2023
Admission routes1
Has abstractyes

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