Predicting research projects’ output using machine learning for tailored projects management
Bibliographic record
Abstract
With the increasing interest and investment in research and development (R&D), the need for more efficient research project management has grown. Accordingly, we built prediction models to classify research projects that were expected to show excellent research output. Specifically, we applied five machine learning techniques to build prediction models. In an empirical analysis of data on research projects funded by South Korea over the last five years (2014–2018), we found that the automated machine learning model (autoML), in which the machine builds the most suitable learning model, shows relatively greater and more robust performance than models based on other techniques. We also established that research funding and project type played the most important roles in predicting excellent research projects. This study is significant because it shows the need for a paradigm shift in building an evidence-based project management system by verifying the utility and applicability of a data-driven approach in R&D project management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".