Developing and implementing 3D printing services in an academic library
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe the development of a 3D printing pilot project and 3D printing library service. Policy development, instruction, and best practices will be shared and explored. Design/methodology/approach – This paper describes the implementation of 3D printing at the University of Regina Library and details successes, failures, and modifications made to better provide 3D printing services. This paper outlines one academic library’s experience and solutions to offering 3D printing for university patrons. Findings – Although 3D printing has been around for a while, it still requires trial and error and experience in order to print successfully. Training and instruction is needed to run the 3D printer and understand how to develop 3D objects that will print successfully. Originality/value – There have been many publications on 3D printing, but few that discuss problem solving, best practices, and policy development. 3D printing provides a way for patrons to learn about new technology and use that technology to help support learning.
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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".