The benefits of buy‐in: integrating information literacy into each year of an academic program
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the integration of information literacy into each year of a Bachelor of Arts and Science (BAS) program at the University of Guelph, Ontario, and to explain the role of librarian mentors in this program. Design/methodology/approach The paper reviews the literature related to mentoring and librarians, explains the BAS program, and outlines the library's integration into the BAS curriculum. It discusses mentoring, assessment, and future goals, and provides some librarians' observations and advice. Findings The paper demonstrates the benefits of librarian‐student mentoring and of integrating information literacy into each year of an undergraduate degree program. Practical implications Since the mentoring of students by librarians is rarely mentioned in the literature, this description of our mentoring program may inspire other librarians to set up librarian‐student partnerships at their institutions. Our successful application of information literacy into every year of a degree program and our partnerships with faculty and students may serve as models for other libraries. Originality/value The experience of the University of Guelph library may show other libraries how to integrate information literacy into a program efficiently and effectively.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".