Human Resource Management in Project Management: Ideas at the Cusp
Bibliographic record
Abstract
Human resource management is an important function when managing projects.Over the years, project management has developed and consolidated a robust theory base that lead to a body of knowledge that is unique to its practice and distinct within the larger management discipline.In this direction, this is an opportune moment to reflect on the themes explored within the project management literature that is specific to managing people.Using 104 peer-reviewed articles extracted from web of science database, 6 research themes have been identified that lie at the intersection of project management and human resource management.Co-occurrence of key words is the methodology employed.The themes are Human resource management as a critical success factor for projects, Project manager as an enabler of project success, Projects human resource as vehicle for organizational competitiveness, Project-Human Resource management as engines of innovation, People management competencies of project manager, and Contextual competencies of project manager.
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 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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".