The future of Project and Program Management. Foresight workshop
Bibliographic record
Abstract
Foreseeing the future is a key input in defining governmental policies, business strategies and professional development plans. Our World is changing rapidly in many different areas: social, technical, political, economical, environmental, organizational, etc. Different methodologies involving a group of persons with knowledge on a subject have shown that they are useful at generating valuable foresight about the subject. What will the role of Project Management (PM) and Program Management (PgM) be in the future? This article presents the basis for a workshop with project managers to address this question. It will be held at PMI North America Global Congress Vancouver October 2012. Key questions about how PM might evolve the future will be addressed in this workshop. Starting with historical and trend information, brain-storming activities, group discussions and by using the Delphi technique we will attempt to anticipate our future World and the role and key aspects of PM. Workshop participants will have the opportunity to: get involved in the task of foreseeing the future get concise information to begin the process learn from their own perceptions and those of other colleagues identify key factors that could impact the future and get ready to monitor their evolution as triggers for change obtain more insights to manage their own strategic decisions enjoy!
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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 | 0.023 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".