The Cunningham Fellowship: three international points of view*
Bibliographic record
Abstract
The Medical Library Association Cunningham Fellowship Program provides funds for one medical librarian per year from outside the United States or Canada to work and learn in United States or Canadian medical libraries for a period of 4 months. An overview of the Cunningham Fellowship is presented from three different points of view-that of a Medical Library Association member who has worked closely with the Cunningham Fellowship programme, and two former Cunningham Fellows. Anita Verhoeven, who relates her impressions of American culture, architecture and art, was the 1998 MLA Cunningham Fellow and visited 33 libraries, met 171 librarians, visited prestigious universities and attended a Medical Library Association meeting. Ioana Robu, the 1997 Cunningham Fellow, visited 15 libraries in 13 cities during her experience. She describes the process of applying for the fellowship and assesses the impact that the 1997 Cunningham Fellowship has made on her life, her library and medical librarianship in Romania. An overview of the Cunningham Fellowship is also given, which includes the history, the application process, the requirements of the fellowship and the time table of the fellowship.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 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".