Citation Analysis

Predicting Financial Literacy via Semi-supervised Learning
David Hason Rudd, Huan Huo, Guandong Xu
https://arxiv.org/abs/2312.10984

Citation mentions

Cited references

Sections

4,713

Words (approx)

References by Citation Intensity

Ordered by composite index (descending). Higher values indicate more intensive citation.

#	Reference	Year	Mentions	Breadth	Sec. Wtd	Share	Composite	Main %
1	Worthington, Andrew C	2006	6	2	8.0	0.214	0.874	100%
2	Lusardi, Annamaria and Mitchell, Olivia S and Curt...	2010	4	2	6.0	0.143	0.811	100%
3	Branco, Paula and Torgo, Lu\'\i	2017	4	2	8.0	0.143	0.811	100%
4	Fazakis, Nikos and Karlos, Stamatis and Kotsiantis...	2019	2	2	4.0	0.071	0.644	100%
5	Ding, Ming and Tang, Jie and Zhang, Jie	2018	2	1	4.0	0.071	0.511	100%
6	Zhou, Zhi-Hua and Li, Ming and others	2005	2	1	4.0	0.071	0.511	100%
7	Huang, R and Tawfik, H and Samy, M and Nagar, AK	2007	1	1	2.0	0.036	0.406	100%
8	Defferrard, Michae	2016	1	1	2.0	0.036	0.406	100%
9	Kipf, Thomas N and Welling, Max	2017	1	1	2.0	0.036	0.406	100%
10	Lin, Wanyu and Gao, Zhaolin and Li, Baochun	2020	1	1	2.0	0.036	0.406	100%
11	Vluymans, Sarah and Vluymans, Sarah	2019	1	1	2.0	0.036	0.406	100%
12	Torgo, Luis and Ribeiro, Rita	2007	1	1	2.0	0.036	0.406	100%
13	Kostopoulos, Georgios and Karlos, Stamatis and Kot...	2018	1	1	2.0	0.036	0.406	100%
14	Krawczyk, Bartosz	2016	1	1	2.0	0.036	0.406	100%

Measures: Mentions = total in-text citations; Breadth = distinct sections; Sec. Wtd = section-weighted count (body ×2, lit review/appendix ×0.5); Share = mentions / total citations in paper; Composite = geometric mean of normalised count, breadth, and main-text ratio; Main % = fraction of mentions in main text (excl. appendix). (self) = self-citation.