Bibliographic record
Abstract
Purpose – The article aims to convey the experiences of installing an Espresso Book Machine (tm) into Windsor Public Library. It relates how an extremely high-tech, mechanical and new process was received in a very traditional field. Design/methodology/approach – Windsor Public Library acquired an Espresso Book Machine, and built around it a Self-Publishing Centre which included iMacs loaded with software, a scanner, a comfortable area and a dedicated staff member. Findings – The creativity that arose from the Self-Publishing Centre was not limited only to individual, solitary authors who wanted just to produce their own works. A network of like-minded people formed to give encouragement and support developed, increasing the opportunities for elevating literacy in our community. Research limitations/implications – Limitations are obviously that we are one small community enjoying the benefits of this machine. It is impossible to predict if other communities and geographic locations would enjoy the same success. Practical implications – Practical implications are that even with the advancing tide of ebooks and non-print matter, patrons are still very eager to consume and produce printed materials. Social implications – The technological marvels of the high-tech gear that have been installed would be lost without the people forming communities around the technology. Originality/value – The results of installing the Self-Publishing Centre were very different from what we anticipated. The products and community established here are as awe-inspiring as the machine at the heart of the Centre.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.033 | 0.032 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".