Alternative Scholarship through Table-top Games: A Practical Demonstration
Bibliographic record
Abstract
Research can take many forms, especially in a field as diverse as library studies. This panel will bring together several emerging scholars as they navigate current Library and Information Science issues through the medium of table-top games. It is our hope that this panel will illustrate how alternative scholarship can be used to teach and explore emerging issues in the field of LIS. La recherche peut prendre de nombreuses formes, en particulier dans un domaine aussi divers que la bibliothéconomie. Ce panel réunira plusieurs chercheurs émergents alors qu'ils explorent les enjeux actuels des bibliothèques et des sciences de l'information à travers les jeux de table. Nous espérons que ce panel illustrera comment des méthodes alternatives peuvent être utilisées pour enseigner et explorer les questions émergentes dans le domaine de la bibliothéconomie et des sciences de l'information.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".