Contextualized Project Management Practice: A Cluster Analysis of Practices and Best Practices
Bibliographic record
Abstract
The specificity of project management in different contexts and industries is recognized, but little empirical research encompasses a sufficiently broad range of contexts and project types to precisely identify these specificities. This article adopts such a wide perspective based on a large sample of data from an ongoing empirical investigation of project management practice. Contextual archetypes are identified (i.e., clusters of experienced practitioners that share similar organizational and project contexts). Archetypes of contextualized practice are then investigated through the study of the extent of use of empirically identified toolsets in each cluster. The results empirically confirm some well-known assumptions about practice but also sharpen the knowledge and understanding of practice in real complex multidimensional contexts. A new concept of “performing-maturity” emerged from the data. This concept sheds light on the entangled imbrications of maturity, competence, and success. Practices are regressed against performing-maturity to reveal best contextualized practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".