Facilitating Student-Authored Papers in LIS Education Research: A Case Study from the LIS Classroom
Bibliographic record
Abstract
This paper describes the process and tools used to facilitate a collaborative student-authored paper that was recently published as a short communication in the Journal of Education for Library and Information Science (JELIS). This paper is written by the instructor of the course and provides direction to other LIS instructors on how to successfully facilitate publishable quality student-authored papers as an in-class activity using online collaborative teaching tools. Cet article décrit le processus et les outils utilisés pour faciliter un article collaboratif co-écrit par des étudiants, récemment publié sous forme de brève communication dans le Journal of Education for Library and Information Science (JELIS). Cet article est rédigé par l'instructeur du cours et fournit des directives aux autres instructeurs de LIS sur la façon de faciliter avec succès des articles de qualité publiables et co-rédigés par des étudiants en tant qu'activité en classe à l'aide d'outils d'enseignement collaboratif en ligne. Il est particulièrement pertinent pour les cours ou les projets qui incluent des sujets de justice sociale.
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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 | 0.055 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".