The University of Zambia is working to improve the visibility and impact of its research output by automating processes for curating scholarly works. Current efforts include developing machine learning models to automatically classify electronic theses and dissertations (ETDs) by domain, type, and subject using features extracted from the manuscripts. Preliminary results show models that can accurately classify ETD collections, types, and subjects have been implemented and deployed as a web API. This aims to address challenges around research output not being properly archived and discoverable, making the impact of research difficult to measure.

1. 2nd
ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020)
January 27–31, 2020 | Cape Town, South Africa
Improved Scholarly Communication Using Machine
Learning
Lighton Phiri | University of Zambia
</> ETD Classifier API: https://github.com/lightonphiri/etd_autoclassifier
Introduction
The University of Zambia is a public research
university in Zambia. While there are numerous
reported research activities at the institution,
measuring the impact of this research is
problematic due to the low online visibility of
research output.
Challenges
○ Research output not properly curated and
archived.
○ Discoverability of research output is
compromised.
○ Measuring the impact of research is difficult.
Current and Future Work
○ Automate manual processes associated with
curation of scholarly research output.
○ Reclassification of curated digital objects.
○ Implement toolkits for facilitating effective
curation of scholarly research output.
○ Bibliometric analyses of scholarly research
output.
<oai_dc:dc>
<dc:title>
Evaluation of research education networks
</dc:title>
<dc:creator>Mwiinga, Jervas</dc:creator>
<dc:subject>
High performance computing--Zambia
</dc:subject>
<dc:subject>
Research education networks--Zambia
</dc:subject>
<dc:description>
[...]
</dc:description>
<dc:date>2018-07-23T13:00:50Z</dc:date>
<dc:date>2017</dc:date>
<dc:type>Thesis</dc:type>
[...]
<dc:format>application/pdf</dc:format>
<dc:publisher>
The University of Zambia
</dc:publisher>
</oai_dc:dc>
Preliminary Results
Preliminary work has focused on exploring techniques for automatically classifying Electronic
Theses and Dissertations (ETDs), using features extracted from the single source of truth: ETD
manuscript.
○ Models for automatically classifying ETD collections, types and subjects implemented.
○ Models deployed as a Flask-based API.
Figure 1. ETD Domain Classification Figure 2. ETD Type Classification