Exploring Canadian academic librarians’ and archivists’ research outside librarianship and archival studies
Bibliographic record
Abstract
Many academic librarians and archivists (L/As) in Canada have research responsibilities as part of their jobs. For some, those responsibilities include research and scholarship in any disciplinary area, including creative works. This study explored the practices and perspectives of academic L/As in Canada with respect to research in areas outside of librarianship or archival studies (LIAS). An invitation to complete an online questionnaire was sent to over 1,800 email addresses and two listservs. The questionnaire asked about the non-LIAS topics that academic L/As have researched, their perceptions about the importance of research outside of LIAS, and barriers or restrictions they may encounter in doing this research. From the 345 usable responses, 85% of respondents have conducted research on LIAS topics, 32% have done non-LIAS research as part of their job, 29% have done non-LIAS research but not as part of their job, and 38% have not done research outside LIAS. Personal interest was the primary reason for doing non-LIAS research. Nearly half of respondents said that doing non-LIAS research and producing or performing creative works were extremely or very important. At the same time, respondents’ comments revealed a range of perspectives about non-LIAS research, including the sense that it is reasonable to have a connection between research and professional work. More attention is needed to develop a shared understanding about the place and value of non-LIAS research. Data from the study are available in Borealis: https://doi.org/10.5683/SP3/SAKC2C.
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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 | 0.046 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.031 |
| Science and technology studies | 0.063 | 0.024 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".