Interlibrary loan in US and Canadian health sciences libraries 2005: update on journal article use
Bibliographic record
Abstract
PURPOSE: The authors analyzed 2.48 million interlibrary loan (ILL) requests entered in the National Library of Medicine's (NLM's) DOCLINE system from 3,234 US and Canadian medical libraries during fiscal year (FY) 2005 to study their distribution and nature and the journals in which requested articles were published. METHODS: Data from DOCLINE and NLM's indexing system and online catalog were used to analyze all DOCLINE ILL transactions acted on from October 2004 to September 2005. The authors compared results from this analysis to previous data collected in FY 1992. RESULTS: Overall ILL volume in the United States and Canada is at about the same level as FY 1992 despite marked growth in online searching, knowledge discovery tools, and journals available online. Over 21,000 unique journal titles and 1.4 million unique articles were used to fill 2.2 million ILL requests in FY 2005. Over 1 million of the articles were requested only once by any network library. Fifty-two percent (11,022) of journals had 5 or fewer requests for articles from all the years of a journal by all libraries in the network. Fifty-two percent of the articles requested were published within the most recent 5 years. CONCLUSION: The overall ILL profile in the libraries studied has changed little since FY 1992, notable given other changes in publishing. Small changes, however, may reveal developing trends. Total ILL traffic has been declining in recent years following a peak in 2002, and fewer of the articles requested were published in the most recent five years compared to requests from 1992.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.058 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".