Is There an “Optimal” Career Path for Humanities and Social Science Graduates in China? A Life-course, Cross-platform Text-as-data Study of Task Priority Sequences

Weixiang Gan, Mengfei Xiao*, Sikun Chen, Xiaolin Song, Tara Ahmed Mohammed
Graduate School of Business, SEGi University, Petaling Jaya, Selangor 47810, Malaysia
*Corresponding email: seanphydiyas@gmail.com
https://doi.org/10.71052/grb2025/AJDD6336

Against the backdrop of continued expansion in China’s higher education, intensified employment competition, and the sustained popularity of public sector recruitment and professional qualification examinations, career choices among humanities and social science groups have become more differentiated and increasingly shaped by widely circulated, template like experiences online. This paper addresses a central question in contemporary China: Whether there is an optimal solution for humanities and social science career pathways. Rather than searching for one universally correct answer, we argue that the key is to identify which options are more cost effective, more stable, and more feasible under specific conditions. To do so, we scraped 58,216 public discussion texts from Zhihu and Xiaohongshu covering 2018 to 2025, cleaned them with Python to obtain 41,372 analysable texts, and conducted in depth coding on a sample of 3,200 items. We focused on sequencing recommendations embedded in narratives, such as what to do first, what to do next, and what happens if a window is missed, and then built an age based map of priority tasks. The findings indicate that aged 18 to 22 emphasise building foundations through internships, projects, and portfolios, while also enhancing visibility through competitions and titles. Aged 23 to 27 often treat postgraduate study and entry threshold credentials as key tools for resetting direction and securing access to desired tracks. Aged 28 to 32 enter a stage of competitive differentiation and commonly stress pursuing credentials or professional titles in parallel with accumulating project based capabilities. Aged 33 to 37 focus more on promotion and professional title advancement by completing documentation requirements and managing institutional timing. Aged 38 and above tend to prioritise stabilisation and repositioning, converting experience into management, consulting, or side business products. Further analysis shows that the optimal solution depends not only on age stage, but also on conditions such as industry entry barriers, organisational promotion rules, regional opportunity structures, and family responsibilities. The study provides individuals and universities with an actionable, stage based task map that can help reduce anxiety driven misallocation of effort.

References
[1] Main, J. B., Prenovitz, S., Ehrenberg, R. G. (2019) In pursuit of a tenure-track faculty position: Career progression and satisfaction of humanities and social sciences doctorates. The Review of Higher Education, 42(4), 1309-1336.
[2] Lerro, A., Schiuma, G., Manfredi, F. (2022) Entrepreneurial development and digital transformation in creative and cultural industries: trends, opportunities and challenges. International Journal of Entrepreneurial Behavior & Research, 28(8), 1929-1939.
[3] Piattoeva, N. (2018) Elastic numbers: National examinations data as a technology of government. Governing by Numbers, 18-36.
[4] Zhang, Y. (2024) Path of career planning and employment strategy based on deep learning in the information age. Plos One, 19(10), e0308654.
[5] Evans, J. (2022) From Text Signals to Simulations: A Review and Complement to Text as Data by Grimmer, Roberts & Stewart (PUP 2022). Sociological Methods & Research, 51(4), 1868-1885.
[6] Kozinets, R. V., Gretzel, U. (2024) Netnography evolved: new contexts, scope, procedures and sensibilities. Annals of Tourism Research, 104, 103693.
[7] Chen, Y., Wu, X., Hu, A., He, G., Ju, G. (2021) Social prediction: a new research paradigm based on machine learning. The Journal of Chinese Sociology, 8(1), 15.
[8] Akkermans, J., da Motta Veiga, S. P., Hirschi, A., Marciniak, J. (2024) Career transitions across the lifespan: a review and research agenda. Journal of Vocational Behavior, 148, 103957.
[9] Salwén, H. (2021) Research ethical norms, guidance and the internet. Science and Engineering Ethics, 27(6), 67.
[10] Cui, H., Yu, P. (2026) Human-generative AI cooperation and knowledge collaboration in online knowledge communities: an analysis based on adaptive structuration theory for individuals. Information Technology & People, 1-38.
[11] Yu, Z. (2025) Xiaohongshu: Modernizing social commerce in China’s digital landscape. Cases on Chinese Unicorns and the Development of Startups, 327-346.
[12] Charmaz, K., Thornberg, R. (2021) The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18(3), 305-327.
[13] Bryda, G., Costa, A. P. (2023) Qualitative research in digital era: innovations, methodologies and collaborations. Social Sciences, 12(10), 570.
[14] Olapane, E. C. (2021) An in-depth exploration on the praxis of computer-assisted qualitative data analysis software (CAQDAS). Journal of Humanities and Social Sciences Studies, 3(11), 57-78.
[15] Dhakal, K. (2022) NVivo. Journal of the Medical Library Association: JMLA, 110(2), 270.
[16] O’Connor, C., Joffe, H. (2020) Intercoder reliability in qualitative research: debates and practical guidelines. International Journal of Qualitative Methods, 19, 1609406919899220.
[17] Braun, V., Clarke, V. (2023) Toward good practice in thematic analysis: Avoiding common problems and be (com) ing a knowing researcher. International Journal of Transgender Health, 24(1), 1-6.
[18] Kitchenham, B., Madeyski, L., Budgen, D. (2022) SEGRESS: software engineering guidelines for reporting secondary studies. IEEE Transactions on Software Engineering, 49(3), 1273-1298.
[19] Drolet, M. J., Rose-Derouin, E., Leblanc, J. C., Ruest, M., Williams-Jones, B. (2023) Ethical issues in research: perceptions of researchers, research ethics board members and research ethics experts. Journal of Academic Ethics, 21(2), 269-292.
[20] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and improvement methods of deep learning ability in blended learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.
[21] Deng, Z., Xiang, H., Tang, W., Cheng, H., Qin, Q. (2024) BP neural network-enhanced system for employment and mental health support for college students. International Journal of Information and Communication Technology Education (IJICTE), 20(1), 1-19.

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Gan, W., Xiao, M., Chen, S., Song, X., Mohammed, T. A. (2025) Is There an “Optimal” Career Path for Humanities and Social Science Graduates in China? A Life-course, Cross-platform Text-as-data Study of Task Priority Sequences. Global Education Bulletin, 2(6), 11-28. https://doi.org/10.71052/grb2025/AJDD6336

Published

20/01/2026