Suggestions for Improving Measurement Plans:
Bibliographic record
Abstract
Time and Cost are most often in industry the two main (often solely) dimensions of analysis against which a project is monitored and controlled, excluding other possible dimensions such as Quality, Risks, impact on society and Stakeholders ’ viewpoints in a broader sense. Another issue of interest is the proper amount of measures and indicators to implement in an organiseation to optimising the two sides of the cost of quality (COQ- cost of quality- and CONQ- cost of non quality). How can multiple concurrent control mechanisms across several dimensions of analysis be balanced? The approach of Balancing Multiple Perspectives (BMP) has been designed to help project managers choose a set of project indicators from several concurrent viewpoints. After gathering experiences from Canada, Germany and Turkey, this paper presents the results from a new BMP application in Spain, using a list of 14 candidate measures interviewing a double set of respondents from industry. Lessons learned are presented for improving measurement plans. 1.
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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 | 0.159 | 0.348 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".