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Record W1510282673 · doi:10.1177/1077800413479559

Rhizoanalytic Pathways in Qualitative Research

2013· article· en· W1510282673 on OpenAlexaff
Diana Masny

Bibliographic record

VenueQualitative Inquiry · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPresentation (obstetrics)Qualitative researchRelation (database)SociologyEpistemologyOrder (exchange)Computer scienceSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

This article deploys the concept of rhizoanalysis in order to disrupt, to think, and to do qualitative research differently. Rhizoanalysis is a concept created out of Deleuze and Guattari’s (1987) concept of the rhizome. The article explores what rhizoanalysis can do, how it functions, and what it produces in relation to research on literacies, in particular multiple literacies theory (MLT). The article is a rhizome having multiple entries: concept creation, rhizoanalysis the concept, an introduction to MLT followed by a detailed presentation of a rhizoanalytic study on conceptualizing writing systems in multilingual children. The article exits with an intermezzo and a potential becoming of rhizoanalysis as an approach to qualitative inquiry.

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)0.109
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.891
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1090.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0080.042
Scholarly communication0.0140.013
Open science0.0030.017
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.490
GPT teacher head0.516
Teacher spread0.026 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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