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Record W2907002198 · doi:10.22545/2018/00109

Philosophical Underpinnings of the Transdisciplinary Research Methodology

2018· article· en· W2907002198 on OpenAlexaff
Sue L. T. McGregor

Bibliographic record

VenueTransdisciplinary Journal of Engineering & Science · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutions3v Geomatics (Canada)
Fundersnot available
KeywordsAxiologyEpistemologyOntologyContradictionEmbodied cognitionValue (mathematics)AxiomSociologyHumanityComputer sciencePhilosophyMathematics

Abstract

fetched live from OpenAlex

This paper is predicated on two assumptions. First, many scholars do not appreciate the philosophical underpinnings of their research, commonly grounded in three dominant methodologies- empirical, interpretive and critical. Second, if scholars were more familiar with a newcomer - transdisciplinary (TD) research methodology - they would be more inclined to embrace it in their research. The paper begins with an explanation of four philosophical axioms (ontology, epistemology, logic and axiology) shaping the aforementioned dominant research methodologies. Then, each Nicolescuian TD axiom is described: (a) multiple levels of Reality mediated by the Hidden Third (ontology); (b) knowledge as complex, emergent, cross-fertilized and embodied (epistemology); (c) inclusive logic (logic of complexity) to facilitate contradiction reconciliation; and (d) transdisciplinary value formation (axiology). The paper concludes with a preliminary overview of Nicolescuian TD research methodology in action. When used in concert with the three longstanding research methodologies, the Nicolescuian approach holds promise for addressing the complexities facing humanity.

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)0.089
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.993
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0070.066
Scholarly communication0.0120.010
Open science0.0030.010
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.328
GPT teacher head0.510
Teacher spread0.181 · 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
DomainMethods
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

Citations26
Published2018
Admission routes1
Has abstractyes

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