Dungeons and downloads: collecting tabletop fantasy role‐playing games in the age of downloadable PDFs
Bibliographic record
Abstract
Purpose This paper aims to provide libraries with collections advice regarding fantasy role‐playing games. Design/methodology/approach Current and emerging publication and sales models of pencil and paper, tabletop fantasy role‐playing games are explored. Details of print, print‐on‐demand, free and purchasable downloads, and subscription‐based options for major fantasy role‐playing games and alternatives are provided. Findings Many options are available to libraries wishing to provide support for fantasy role‐playing game programming. While an overwhelming quantity of publications are often available for purchase, usually only a bare minimum is required to run a role‐playing game. Free or modestly priced options are available for libraries on a shoestring budget. Libraries interested in supporting fantasy role‐playing game programming with collections need not spend much. Spending less on collections requires a greater amount of imagination, socializing, creativity, collaboration and literacy on the part of program participants. Originality/value Many libraries are interested in supporting fantasy role‐playing games with collections, but do not know where to start. While much is being written about gaming in libraries, little has been written to help libraries navigate current role‐playing game book publication and sales models.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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".