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Record W2896258807 · doi:10.19255/jmpm359

ProdJecting the Future: New Product-Project Development: The Prod-Ject Management System

2018· article· en· W2896258807 on OpenAlexaff
Ismail Albaidhani, Alejandro Romero-Torres, Brahim Meddeb

Bibliographic record

VenueJournal of Modern Project Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsScope (computer science)Process managementProject management triangleProject managementNew product developmentProduct lifecycleProduct (mathematics)Work (physics)Relevance (law)SustainabilityComputer scienceSystems engineeringKnowledge managementEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

Several new theoretical models suggest integration between the creativity and implementation activities for a comprehensive innovation cycle and complete project phases. However, organizations need more guidance to improve the project/product success rate. Therefore, the empirical research discussed in this paper revealed that the two variables (idea creation & Project delivery) are actually linked and could be considered for possible integration. A new and more practical management system ProdJect was also unleashed that detailed how the two variables could be operated with detailed processes, systems, roles and organizational design. The ProdJect management system offers a detailed and comprehensive purpose-to-impact cycle, giving a new and unique evaluation model for the project and product development type that looks at effectiveness, relevance, and overall sustainability instead of focusing on limited aspects of work such as time, cost and scope.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0060.007
Open science0.0010.003
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 head0.062
GPT teacher head0.286
Teacher spread0.224 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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