The Progression Towards Project Management Competence
Bibliographic record
Abstract
The purpose of this research was to investigate the soft competencies by project phase that IT project managers, hybrid and technical team members require for project success. The authors conducted qualitative interviews to collect data from a sample of 22 IT project managers and business leaders located in Calgary, Canada. They identified the key competencies for the three types of job roles. The research participants offered their opinions of what are the most important competencies from the following competence categories: Personal Attributes (e.g. eye for details), Communication (e.g. effective questioning), Leadership (e.g. create an effective project environment), Negotiations (e.g. consensus building), Professionalism (e.g. life long learning), Social Skills (e.g. charisma) and Project Management Competencies (e.g. manage expectations). The authors discuss the progression of competence through these job roles. They identified and discuss the interplay between a change in job role and the required competencies necessary for IT project success from a neuro-science perspective.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".