Machine Learning to Assess Trends and Alignment of Funded Research Output | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=xmg3sWReEMY


Duration: 56:31
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4

Speaker(s): Ashkan Ebadi
Facilitator(s): Amir Feizpour

Find the recording, slides, and more info at https://ai.science/e/machine-learning-to-assess-trends-and-alignment-of-funded-research-output--Bz5MK8Jo38B6ebwG40b6

Motivation / Abstract
Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country.

Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada.

What was discussed?
- Using Structural Topic Modelling to extract high level research topics out of scientific publications
- Nuances of extracting topics and generating labels for clusters in a semi-automated way
- Implications of this work on automated research tracking for the purposes of research & development policy making

What are the key takeaways?


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