Bibliographic record
Abstract
The Future Voices in Public Services column is a forum for students in graduate library and information science programs to discuss key issues they see in academic library public services, to envision what they feel librarians in public service have to offer to academia, to tell us of their visions for the profession, or to tell us of research that is going on in library schools. We hope to provide fresh perspectives from those entering our field, in both the United States and other countries. Interested faculty of graduate library and information science programs who would like their students' ideas represented in these pages are invited to contact Nancy H. Dewald at nxd7@psu.edu. Martha Stortz is a student in the Library and Information Science (LIS) program at the University of Western Ontario. In this essay she offers her perspective on the teaching of librarianship. The University of Western Ontario's LIS program is part of the Faculty of Information and Media Studies (FIMS) and enjoys the benefits of interdisciplinarity brought about by collaboration with other FIMS programs such as Journalism and Media Studies. Originally founded as the independent School of Library and Information Science in 1967, the school merged with other programs in 1996 to form FIMS. Two major LIS programs of study are offered: one leading to the Master of Library and Information Science (MLIS) and the other to the Doctor of Philosophy (PhD). The MLIS program is accredited by the American Library Association. *****
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".