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Record W3185955886 · doi:10.3390/su13158502

Complex System Governance as a Framework for Asset Management

2021· article· en· W3185955886 on OpenAlex
Polinpapilinho F. Katina, James C. Pyne, Charles B. Keating, Dragan Komljenović

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

VenueSustainability · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsLeverage (statistics)Corporate governanceAsset (computer security)Asset managementProcess managementValue creationBusinessClosure (psychology)ScarcityValue (mathematics)Risk analysis (engineering)Computer scienceKnowledge managementEconomicsFinance

Abstract

fetched live from OpenAlex

Complex system governance (CSG) is an emerging field encompassing a framework for system performance improvement through the purposeful design, execution, and evolution of essential metasystem functions. The goal of this study was to understand how the domain of asset management (AsM) can leverage the capabilities of CSG. AsM emerged from engineering as a structured approach to organizing complex organizations to realize the value of assets while balancing performance, risks, costs, and other opportunities. However, there remains a scarcity of literature discussing the potential relationship between AsM and CSG. To initiate the closure of this gap, this research reviews the basics of AsM and the methods associated with realizing the value of assets. Then, the basics of CSG are provided along with how CSG might be leveraged to support AsM. We conclude the research with the implications for AsM and suggested future research.

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)0.004
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.436
Teacher spread0.329 · 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