Project management assets and project management performance: Preliminary findings
Bibliographic record
Abstract
There were nearly 500 attendees at the Portland International Centre for Management of Engineering and Technology conference. Over 760 papers were submitted and 344 were accepted. We presented our paper in the Project/Program Management Division to an audience of 40. A number of those in attendance were masters and doctoral students. The combined feedback on our paper/presentation from the audience was extremely helpful. The feedback reinforced the methodological rigor our study and helped us in clarifying several aspects as we work on a revised version of the paper to submit to the Management Research journal. During the evening reception, several individuals thanked us for my presentation and indicated that they found it to be clear and easy to follow. I would like to take this opportunity to sincerely thank the A&PDF committee for the generous funding and support to attend this conference and present at it. I thoroughly enjoyed the tracks, especially the constructive dialogue we were able to engage in at the sessions to assist each other with our research endeavors. This conference also enabled me to reconnect with my close research colleague and others in the project management field. I was also able to network with and meet new colleagues. Thank you again. I appreciate all the support from AU. As per page 24 of the proceedings (Source: http://www.picmet.org/new/conferences/2011/Picmet11.pdf) THE PAPERS “Research papers and applications-oriented papers are explicitly identified in this conference. Separate evaluation criteria were used, and different referees were selected for each category to make sure that appropriate papers were included in the conference for the “Research” and “Application” categories. We emphasized research methodology, the use of the research literature, the theory behind the paper, the sample size, and the impact on the research community for the “Research Papers.” The important evaluation criteria for “Industry Applications” were the usefulness of the application, the importance of the case being discussed, the generalizability of the concepts presented, and the impact of the paper on the users of technology management. The “Research Papers” included in PICMET ’11 are listed with an [R] in front of their titles on the following pages; and the “Industry Applications” papers are shown with an [A] in front of their titles. Roughly 73 percent are in the [R] category, and the rest are in the [A] category. The Research Papers and Industry Applications are mixed in the sessions. This was done intentionally to assure effective exchange of ideas among those presenting research papers and those presenting applications-oriented papers.”
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".