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Record W4283575947 · doi:10.9734/bpi/rdass/v5/2329b

Recent Study on Sustainable Program Management: Hierarchical Causal Systems

2022· book-chapter· en· W4283575947 on OpenAlexaff
Bongs Lainjo

Bibliographic record

VenueBook Publisher International (a part of SCIENCEDOMAIN International) · 2022
Typebook-chapter
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsCentre de Santé et de Services Sociaux Cavendish
Fundersnot available
KeywordsMaslow's hierarchy of needsTransparency (behavior)AccountabilitySustainabilityHierarchyProcess managementContext (archaeology)Computer scienceOrder (exchange)Conceptual frameworkManagement scienceBusinessKnowledge managementRisk analysis (engineering)EngineeringPolitical scienceSociologyPsychologyComputer securityGeographySocial scienceSocial psychologyFinance

Abstract

fetched live from OpenAlex

The goal of this research article is to improve program management protocols. The technique can aid in the reduction of subtleties, duplication, and redundancies. Seven components have been addressed in this context for supporting the attainment of long-term management of a development program. As a result, the "CARROT-BUS" model's conceptual framework was taken into account when conducting this research. CARROT stands for Capacity, Accountability, Resources, Results, Ownership, and Transparency, and it emphasizes environmental enabling, whereas BUS is seen as a bottom-up technique. As a result, this holistic and causal model can be seen as theoretically equivalent to Abraham Maslow's hierarchy of needs framework. Finally, secondary sources were heavily considered when doing this research. Based on the data, it can be concluded that one of the major features that institutions all over the world maintain in order to attain anticipated and compelling outcomes is sustainability.

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)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.014
Science and technology studies0.0020.008
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.112
GPT teacher head0.384
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
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

Citations0
Published2022
Admission routes1
Has abstractyes

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