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Record W4328126975 · doi:10.1111/emre.12568

A Manifesto for project management research

2023· article· en· W4328126975 on OpenAlexaff
Giorgio Locatelli, Lavagnon A. Ika, Nathalie Drouin, Ralf Müller, Martina Huemann, Jonas Söderlund, Joana Geraldi, Stewart Clegg

Bibliographic record

VenueEuropean Management Review · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à MontréalUniversity of Ottawa
Fundersnot available
KeywordsManifestoBusinessManagementPolitical scienceSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Project management research has evolved over the past five decades and is now a mature disciplinary field investigating phenomena of interest to academics, practitioners and policymakers. Studies of projects and project management practices are theoretically rich and scientifically rigorous. They are practically relevant and impactful when addressing the pursuit of operational, tactical and strategic advancements in the world of organisations. We want to broaden the conversation between project management scholars and other scholars from cognate disciplines, particularly business and management, in a true scholarship of integration and cross‐fertilisation. This Manifesto invites the latter scholars to join efforts providing a foundation for further creative, theoretical and empirical contributions, including but not limited to tackling grand challenges such as climate change, pandemics, and global poverty. To this end, we identify five theses: Projects are often ‘agents of change’ and hence fundamental to driving the innovation and change required to tackle grand challenges. Much project management research leverages and challenges theories across disciplines, including business, organisation and management studies, contributing to developing new theories, including those specific to projects and temporary organisations. ‘Projects’ are useful units of analysis, project management research is ideal for scientific cross‐fertilisation and project management scholars welcome academics from other communities to engage in fruitful conversations. As in many other fields of knowledge, the project management research community embraces diversity, welcoming researchers of different genders and various scientific and social backgrounds. Historically rooted in ‘problem‐solving’ and normative studies, project management research has become open to interpretative and emancipatory research, providing opportunities for other business, management and organisational scholars to advance their knowledge communities.

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)0.088
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.465

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0120.027
Scholarly communication0.0320.018
Open science0.0030.018
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0200.006

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 head0.596
GPT teacher head0.570
Teacher spread0.027 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations125
Published2023
Admission routes1
Has abstractyes

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