Bibliographic record
Abstract
An Examination and Application of Non-Traditional Collections and Recreational Play in the Academic LibraryLibraries have traditionally existed for the purpose of providing information to patrons, generally in the form of traditional print collections consisting of books and periodicals.The role of the library, however, has shifted in recent years.Several trends have emerged, including: the use and acceptance of electronic resources and the resultant decline in the circulation of print materials, a reduction in reference services, and, in the case of academic libraries, a major shift from independent learning towards collaborative learning (Gayton, 2008).Another key trend is becoming more prevalent in libraries--non-traditional collections.This article will briefly introduce non-traditional library collections, illustrate personal experiences and best practices as a board game librarian, and highlight implications of collecting non-traditional items and programming during the COVID-19 pandemic. Non-Traditional Library CollectionsPut simply, non-traditional collections are those that extend beyond traditional items, including seed libraries/seed shares, crafting kits, tools, sporting and fitness equipment, science kits (e.g., Arduino, Raspberry Pi), musical instruments, puzzles, and board games.Libraries collect these items for several reasons:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.049 |
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".