Think Locally, Work Globally: International Document Delivery and CISTI
Bibliographic record
Abstract
Librarians frequently express their reluctance to get involved in international lending. This can perhaps be attributed to a feeling that their interests lie closer to home. As a result, they tend to view requests from libraries in other countries as an exceptional problem to be solved rather than part of the regular workflow. A few bad experiences with international lending can forever color the feeling of interlibrary loan offices about requests from abroad. The general feeling is a dismissive “Why bother?” CISTI has taken a more optimistic view of international resource sharing. CISTI has, in fact, taken up the challenge of supplying to libraries outside of North America, realizing that as the world shrinks, along with library budgets, we must be prepared to look beyond our own borders. This article provides a review of CISTI's experiences and some practical advice for dealing with particular issues, such as lending across long distances, language barriers, messaging and ordering systems, international mail and Customs controls and billing issues.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".