Academically Informed Creative Writing in LIS Programs and the Freedom to be Creative
Bibliographic record
Abstract
This article makes a case for the inclusion of creative writing in Library & Information Science (LIS) courses. Using an example of the course on reading practices and audiences, it shows how creative writing can contribute to the development of creativity, critical thinking, ability for self-direction and independent learning—all the necessary skills for future leaders in the field of LIS. It presents a unique professor–student perspective on creative writing assignments and focuses on two case studies: the role of creative writing in validating students’ personal experiences and previous educational background; and the importance of creative writing in fostering a skill of meaningful and engaging storytelling. It also analyzes several possible concerns related to the incorporation of creative writing in LIS courses, with a particular emphasis on the rigorous but flexible evaluation methods of students’ creative work. The article will be of interest to LIS educators and LIS students, who, it is hoped, would become more involved in the direction of their graduate education.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".