Research Design and Research Systems: An Application of Agent-Based Modelling to Research Funding
Bibliographic record
Abstract
Governments nurture their multi-disciplinary innovation systems by funding several public organizations to help universities and research institutes support research projects and associated infrastructure.To study the impact of research funding, a generic stylized model is developed using Agent-Based Modelling (ABM) to simulate the outcomes.To provide context, the analysis anchors the problem in the context of Genome Canada's research funding efforts.The process of academic research and the impact of grants on its speed and output (papers published) is simulated.To compare the outcomes for policy choices, two measures or indices are developed for the outcomes: efficiency is measured by number of papers per granted money and equity is measured by a Gini coefficient (for papers and money granted); the Matthew effect is also tested to check for effects on equity.Defining academic investigators as the main agent and having investigations and grants as subagents, along with assumptions for the procedures and parameters, an ABM is designed in which investigators conduct individual research using grant and non-grant funds.The simulation model is then tested and verified to be used for evaluation and comparison of policy scenarios.The results revealed that the instruments of allocated budget per competition, the gap between competitions, the sum granted for any proposal, and the size of the target group may be utilized to improve the efficiency and equity of the system.However, there is usually a trade-off between these two objectives and a loss in one of them is necessary to achieve a gain in the other.The tools can be combined in order to secure better results, but there are other factors that should be taken into account in making decisions.Although some lessons can be learned from such a simple model, making it applicable to policy making and to realworld issues, other factors such as investigator heterogeneity, collaborations, and grant administration complexities should be taken into account.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".