Leveraging Machine Learning to Investigate the Impact of NSERC Funding Programs on Research Outcomes
Bibliographic record
Abstract
This research examines the impact of various funding programs by NSERC on research outcomes. We utilize statistical models and machine learning algorithms trained on the integrated database of researchers’ publications and funding to determine the efficacy of NSERC funding programs. We aim to evaluate the effectiveness of different strategies defined by NSERC through funding programs and analyze the impact of various factors. We seek to enhance our understanding, with the aspiration that it will inform the design of more effective programs in the future. \nWe compare the results of linear regression, random forest, and neural networks. Then, we perform SHAP analysis to identify the most important features within funding programs. We aim to gain insights into the impact of receiving funding through different programs on research outcomes. \nWe observed that random forest model outperformed the other models for all dependent variables, i.e., future productivity, quality of the publication, and future co-authorships. Subsequently, we examined the significance of independent variables in predicting dependent variables across the funding programs. \nFor Canada Research Chairs recipients, the impact of their prior work holds greater importance in shaping research outcomes, underscoring a distinctive emphasis on research excellence within this program. In contrast, the impact of career age is lower compared to other programs. Interestingly, within the Discovery Grants program, career age becomes notably influential in predicting future productivity in favor of young researchers. Furthermore, we found an intriguing exception for researchers with a history of large group collaborations within Discovery Grants, where some experience a negative impact on future collaborations. The award amount plays a more important role in shaping the research outcomes of recipients engaged in strategic projects. \nOur findings emphasize the importance of allocating funding programs to researchers whose qualifications are aligned with the programs’ objectives.
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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 | 0.070 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".