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Record W4309927946 · doi:10.1177/87569728221131254

A Multilevel Governance Model for Interorganizational Project Networks

2022· article· en· W4309927946 on OpenAlexaff
Christine Unterhitzenberger, Ralf Müller, Anne Live Vaagaasar, Yongjian Ke, Раймонда Алондериене, Inga Minelgaitė, Margarita Pilkienė, Linzhuo Wang, Fangwei Zhu, Nathalie Drouin, Alfredas Chmieliauskas, Saulius Šimkonis, Mylene Mongeon

Bibliographic record

VenueProject Management Journal · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsKamloops Art Gallery
Fundersnot available
KeywordsCorporate governanceProject governanceMulti-level governanceNetwork governanceBusinessMultilevel modelInformation governanceKnowledge managementProcess managementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study operationalizes and tests a multilevel governance model for interorganizational project networks. Results of a qualitative multicase study are used to develop a framework model with three levels of governance, namely metagovernance, governance of networks, and network governance. This framework is validated through a global survey with 225 responses. Type I and Type II governance are confirmed as the organizational elements of network governance, and the relationships between the different levels are established. Metagovernance directly impacts network governance and this relationship is mediated through governance of networks for Type I governance and moderated through governance of networks for Type II governance.

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)0.004
metaresearch head score (Gemma)0.012
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
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 head0.123
GPT teacher head0.377
Teacher spread0.254 · 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
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

Citations24
Published2022
Admission routes1
Has abstractyes

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