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Record W4211005606 · doi:10.18778/1733-8077.3.2.06

Grounded Theory and Autopoietic Social Systems: Are They Methodologically Compatible?

2007· article· en· W4211005606 on OpenAlexaff
Richard C. Mitchell

Bibliographic record

VenueQualitative Sociology Review · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsBrock University
Fundersnot available
KeywordsGrounded theoryAutopoiesisEpistemologySociologySet (abstract data type)Systems theorySocial scienceComputer scienceQualitative researchPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

The paper offers a secondary analysis from a grounded theory doctoral study that reconsiders its “grounded systemic design” (Mitchell, 2005, 2007). While theorists across multiple disciplines fiercely debate the ontological implications of Niklas Luhmann’s autopoietic systems theory (Deflem 1998; Graber and Teubner 1998; King and Thornhill 2003; Mingers 2002; Neves 2001; O’Byrne 2003; Verschraegen 2002, for example), few investigators have yet to adopt his core constructs empirically (see Gregory, Gibson and Robinson 2005 for an exception). Glaser’s (1992, 2005) repeated concerns for grounded theorists to elucidate a “theoretical code” has provided an additional entry point into this project of integrating grounded theory with Luhmann’s abstract conceptual thinking about how global society operates. The author argues that this integration of methodology and systems thinking provides an evolution of grounded theory – rather than its ongoing “erosion” as Greckhamer and Koro-Ljungberg (2005) have feared – and a transportable set of methodological and analytical constructs is presented as a basis for further grounded study.

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)0.116
metaresearch head score (Gemma)0.157
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: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.615

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1160.157
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0080.014
Science and technology studies0.0040.031
Scholarly communication0.0150.019
Open science0.0040.009
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.216
GPT teacher head0.539
Teacher spread0.322 · 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
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

Citations7
Published2007
Admission routes1
Has abstractyes

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