An Empirical Identification of Project Management Toolsets and a Comparison among Project Types
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.
Bibliographic record
Abstract
This article presents the results of an empirical investigation of project management practice. Practice is investigated through the study of the extent of use of a large number of practices, tools, and techniques specific to project management. A sample of 2,339 practitioners participating in a large-scale international survey is used for this article. The sample size and the diversity of contexts in which the respondents are working render the analysis feasible and the results reliable. The data is analyzed to identify patterns of practice. More specifically, using principal component analysis, the research identifies patterns that demonstrate that practitioners use project management tools and techniques in groups or “toolsets.” A brief attempt is made to compare results with A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (PMI, 2008) Knowledge Areas and Process Groups. The article also shows how practice varies with the management of different types of projects: engineering and construction; business and financial services; information technology (IT) and telecommunications; and software development projects. The identification of these variations has important consequences for practice and for the study of practice.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it