Summer of writing: Supporting the research and publishing needs of academic librarians
Bibliographic record
Abstract
Academic librarians are often expected to publish within the field of library and information sciences (LIS). This pursuit can be challenging, given the lack of research support both in the workplace and incorporated into the Master of Library and Information Sciences (MLIS) degree. To address this research skills gap for LIS professionals, we developed a workshop series which we led in the summer of 2023, based on Belcher's book Writing your Journal Article in Twelve Weeks . This program was tailored to LIS research. For our program, each week, we focused on different goals, from working on arguments to editing our manuscripts. Week by week, participants built and assessed their manuscripts, relying on a collaborative approach to offer support. This paper takes an autoethnographic approach to the case study method, incorporating reflections of the researchers along with a robust literature review. This literature review shows a portrait of the needs, challenges, and supports offered to academic librarians. In this case study, we explore how Belcher's book can be adapted to the LIS context, examining the strengths and weaknesses of applying this resource to this discipline. The paper offers practical and theoretical takeaways from this program for others seeking to run similar, LIS-specific programming, drawing from the literature review and the case study to identify key areas of focus for supporting new LIS researchers. This paper demonstrates that there is a significant need for research and writing support, as well as fostering a sense of community among academic librarians.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".