Why Librarianship? A Comparative Study Between University of Tsukuba, University of Hong Kong, University of British Columbia and Shanghai University
Bibliographic record
Abstract
Career decisions are motivated in part by our internal values, but also are influenced strongly by innumerable external forces perceived in the context of our lives. In the research reported here, we explore various social, cultural, economic and educational factors, as well as personal and professional reasons that influence students in choosing Library and Information Science (LIS) professions as a career. Master of Library and Information Science (MLIS) students from four universities located in four different countries were invited to take part in an online questionnaire survey. The universities were Shanghai University (SHU), the University of British Columbia (UBC), the University of Hong Kong (HKU) and the University of Tsukuba (UT). In total, 175 self-completed questionnaires were collected. Survey results indicated that students enrolled in MLIS programmes were predominately female. Differences and similarities were encountered for the different sites. For example HKU and UBC had the largest number of students with graduate-level qualifications prior to entering the MLIS programme; and students at HKU and UBC tended to vary widely in terms of their educational and occupational backgrounds. For the majority of the HKU and UBC respondents, the decision to obtain a professional qualification in LIS was driven by the desire to maximize the benefits of a career change or for career advancement, while the majority of respondents at the UT and SHU did not have a job or much work experience. While the total surveyed populations are small; the study will be of interest and value to LIS educators and administrators responsible for recruiting MLIS graduates and hiring LIS professions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".