Discussion of metrics for distributed project management: preliminary findings
Bibliographic record
Abstract
Project-based activities are at the heart of the virtual organization concept, which implies working in a limited timeframe with distributed teams. Although distributed projects provide major benefits in terms of tapping partners' competencies, they represent a significant challenge for coordinating and monitoring team performance. The paper investigates distributed projects with a specific focus on performance metrics. This topic is central to performance measurement and control, particularly in cases where several organizations are involved. At this stage, very few studies have looked at this issue. We offer an exploratory discussion based on three dimensions that could eventually assist in the development of performance metrics for distributed projects. These dimensions refer to the concepts of project value chain, balanced scorecards, and a focus on the end user's requirements. The dimensions are explored using the example of a re-manufacturing project: a typical industrial project involving a series of actors working together in a distributed mode.
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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 | 0.087 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".