Bibliographic record
Abstract
This critical literature review identifies the motivations of students entering the Library and Information Studies/Science (LIS) profession and associated Master’s (MLIS) programs, the current knowledge of students and librarians with science backgrounds in LIS fields, and the intersections of these two areas into recruitment research for LIS professionals with science backgrounds. A critical literature review was conducted, with clearly relevant literature included. In general, incoming MLIS students tend to be in the process of changing careers, and they are motivated to pursue LIS due to a combination of intrinsic and extrinsic factors related to their individual contexts. While educational diversity benefits the entire discipline and workforce, science librarianship specifically benefits from having MLIS graduates with science backgrounds. It is expected that the increased complexity and data services focus of science librarianship may also be well served by those with science backgrounds. Recruitment suggestions for increasing the representation of students with science backgrounds in MLIS programs tend to be mere concepts or substantial program investments, without many practical recommendations or real-life examples. Notably, there is a gap in investigations for the Canadian context, and so an exploratory investigation of the motivations and aspirations of students in Canadian MLIS programs, beyond the literature review presented here, should be conducted in the future with a specific focus on identifying and investigating the population of students coming into these programs with science education backgrounds.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".