LIS Program Representatives’ Perspectives on Preparing Students for Careers in Research Data Management and Data-Related Librarianship
Bibliographic record
Abstract
Objective – This study aims to contribute a qualitative analysis of the perspectives of LIS program representatives on providing research data management (RDM) and data librarianship training opportunities to their students. The primary objectives of the study are to determine which programs currently provide training opportunities for students in RDM and related areas, as well as whether programs have provided such opportunities in the past and/or intend to do so in the future. Methods – This study incorporates in-depth qualitative empirical evidence in the form of five semi-structured interviews of representatives of Canadian LIS programs to investigate first-hand perspectives on the RDM and data-related opportunities they can provide to their students. Results – The interviews identified five major themes related to LIS programs’ RDM and data-related training offerings, including the range of formal and informal opportunities currently available in the programs; the ways in which the representatives would mentor and advise students interested in RDM or related career paths; the challenges posed by both the lack of instructors for RDM and data-related courses, and the lack of students who are interested in, or ready to pursue, data-related careers; the need for programs to develop a curriculum that meets the requirements of many stakeholders; and the effects of the rapidly changing library landscape on LIS curriculum development. Conclusion – This qualitative study sheds light on both the support that Canadian LIS programs can provide to students who are interested in RDM and data-related careers in academic libraries, and the challenges those programs face in providing that support.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".