Bibliographic record
Abstract
Program objective The objective of this course (GLIS691 Bioinformatics) was to provide formal bioinformatics education within a master of library and information studies (MLIS) program. As bioinformatics becomes increasingly integral to biomedical research, there is a need for librarians to expand their practice into the domain of bioinformatics, supporting the efficient and accurate use of these complex resources. We developed this course, the first such course offered in a Canadian library school, in response to the demand for librarians to be able to support bioinformatics information needs. Setting The course was offered in the winter term of 2005 in the Graduate School of Library and Information Studies, McGill University. Participants Course participants were MLIS students. Program The course took a library and information science perspective to bioinformatics. The goal was to provide students with the skills and knowledge to provide information services in the domain of bioinformatics and to collaborate in the design and development of bioinformatics resources. This included understanding the field of bioinformatics and the range of resources, the needs and requirements of user groups, practical searching skills, the creation of resources, and the role of the librarian. Conclusions This course represents one approach to providing formal bioinformatics education for librarians. Librarians who are knowledgeable and proficient in bioinformatics will be able to expand the role of the library into this domain; apply their knowledge, skills, and expertise in a complex, chaotic information environment; and develop the essential role of the librarian in the domain of bioinformatics.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".