Framework for identification of performance metrics for research and development collaborations: Construction Innovation Centre
Bibliographic record
Abstract
Purpose The purpose of this study is to provide a framework to identify performance metrics for evaluating research and development collaborations. Design/methodology/approach The framework is developed through a review of similar centres and academic studies, followed by surveys and interviews of researchers and industry practitioners for the case of the Construction Innovation Centre (CIC). The proposed framework consists of identification of existing industry research and development needs, development of a research roadmap representing top research priorities, and identification of the most important services to provide to industry partners, which form the context for defining performance evaluation metrics. Findings A research roadmap is presented, outlining top research areas and methods and a list of the most in-demand services including research, practical and training and outreach services. Metrics for evaluating the performance of proposed projects, completed projects and a collaborative research centre are also identified. Originality/value This study presents a novel approach to defining performance metrics for the evaluation of research and development collaborations. The approach and findings of this study can be adopted by other collaborative research centres and initiatives around the world to develop effective metrics for performance measurement. The proposed framework provides a platform for defining performance metrics in the context of the research roadmap and top-priority services applicable to the research and development collaboration.
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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 | 0.104 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.042 | 0.028 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".