Arranging the Pieces: A Survey of Library Practices Related to a Tabletop Game Collection
Bibliographic record
Abstract
Abstract Objective – The purpose of this study is to explore collection development, cataloguing, processing, and circulation practices for tabletop game collections in libraries. This study used the term “tabletop games” to refer to the array of game styles that are played in real-world, social settings, such as board games, dice and card games, collectible card games, and role-playing games. Methods – An online survey regarding tabletop games in libraries was developed with input from academic, public, and school librarians. Participants were recruited utilizing a snowball sampling technique involving electronic outlets and discussion lists used by librarians in school, public, and academic libraries. Results – One hundred nineteen libraries answered the survey. The results show that tabletop games have a presence in libraries, but practices vary in regard to collection development, cataloguing, processing, and circulation. Conclusion – Results indicate that libraries are somewhat fragmented in their procedures for tabletop collections. Libraries can benefit from better understanding how others acquire, process, and use these collections. Although they are different to other library collections, tabletop games do not suffer from extensive loss and bibliographic records are becoming more available. Best practices and guidance are still needed to fully integrate games into libraries and to help librarians feel comfortable piloting their own tabletop collections.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".