Motivations of choosing archival studies as major in the iSchools: viewpoint between two universities across the Pacific Ocean
Bibliographic record
Abstract
Purpose This study explores the learning and career motivation of the students who have chosen archival studies as their major in their master's degree programs, which has scant prior research. Design/methodology/approach The authors use a qualitative interview method to investigate the students' opinions and underlying reasons. Nine students from the University of Hong Kong (HKU) and University of British Columbia (UBC), both members of the iSchools, were interviewed. Considering the responses and research questions, the authors applied content analysis techniques to summarize data gathering from interviews into five themes to better interpret the meanings behind them. Findings Despite different development stages of archives sectors in Hong Kong and Canada, the learning and career motivation factors of these students from both universities share some similar characteristics and can also be divided into intrinsic factors (such as personal interests, personalities) and extrinsic factors (such as prior working experience, working environment, nature of archives work and development of the archives field). Both intrinsic and extrinsic factors significantly influenced them in choosing archival studies as major in their graduate studies. Practical implications These findings can help educators and professions review and improve the curricula as well as promote the profession to the public and attract more people to pursue their studies in the archives field. Originality/value Scant studies discussed the career development and education motivation of archivists, especially related to Asia.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".