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Record W4309673227 · doi:10.1002/sres.2917

Systems approach in dynamic capabilities

2022· article· en· W4309673227 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSystems Research and Behavioral Science · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsWestern University
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsDynamismSoft systems methodologyViable system modelSystems thinkingKnowledge managementComputer scienceDynamic capabilitiesOntologyProcess (computing)Process managementPerspective (graphical)PerceptionManagement scienceSystems engineeringArtificial intelligenceInformation systemEngineeringCyberneticsPsychologyEpistemologyManagement information systems

Abstract

fetched live from OpenAlex

Abstract The ontology of dynamic capabilities (DCs) is grounded in a systemic perspective of organisational strategy. In a controversial move, DCs theory adopts systems thinking as a metaphorical reference, not a possible research method. Systemic methodologies can provide a holistic management perception and guide managers to develop DCs differently, considering the deliberate learning and design process as a non‐linear dynamism of causal loops. Calling attention to the conceptual origins, this work proposes a framework based on systemic methodologies to manage and develop organisational DCs. Based on two different systemic methodologies, the viable system model (VSM) and soft systems methodology (SSM), we integrate the systems approach of learning and design into DCs management guidelines.

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.

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)0.045
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.427
GPT teacher head0.526
Teacher spread0.099 · 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