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Record W1991975726 · doi:10.1108/01443570410514858

Manufacturing strategy: understanding the fitness landscape

2004· article· en· W1991975726 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

VenueInternational Journal of Operations & Production Management · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFitness landscapeRelevance (law)Computer sciencePerspective (graphical)Complex adaptive systemEvolutionary game theoryManufacturingManagement scienceProcess managementBusinessGame theoryIndustrial organizationEconomicsMarketingArtificial intelligenceMicroeconomicsSociology

Abstract

fetched live from OpenAlex

This theoretical paper presents, extends and integrates a number of systems and evolutionary concepts, to demonstrate their relevance to manufacturing strategy formulation. Specifically it concentrates on fitness landscape theory as an approach for visually mapping the strategic options a manufacturing firm could pursue. It examines how this theory relates to manufacturing competitiveness and strategy and proposes a definition and model of manufacturing fitness. In accordance with fitness landscape theory, a complex systems perspective is adopted to view manufacturing firms. It is argued that manufacturing firms are a specific type of complex system – a complex adaptive system – and that by developing and applying fitness landscape theory it is possible to create models to better understand and visualise how to search and select various combinations of capabilities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.269
Teacher spread0.230 · 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