Bibliographic record
Abstract
Abstract Academic integrity and information literacy concepts are interwoven throughout academic processes. In Canada these are reflected in both secondary and postsecondary assessment environments. Academic librarians are well positioned within and beyond the academy to promote a culture of academic integrity to post-secondary and high school students. In 2008 authors Drinan and Bertram Gallant addressed the opportunity for librarians to take an active role in building a culture of integrity stating “The issue of plagiarism is one that cries out for the active participation of librarians not only in the academic integrity systems on their respective campuses, but also in the national and international academic integrity movement” (p. 137). The question is where are librarians in this movement in Canada? Beyond plagiarism, how far have librarians come in their involvement in academic integrity culture both on and off Canadian campuses? This chapter will look beyond the librarian's role in teaching information literacy and its principles in the classroom to further examine the inroads being made as an active partner with campus services and students in our communities. Connections between ACRL's six information literacy frames and academic integrity with a specific focus on “Scholarship as Conversation” and the role students' play in this process will be highlighted. Librarians have a pivotal role to play in moving the academic integrity conversation forward. Through their understanding of critical and ethical use of information they can be at the forefront of advocating for integrity in academic work and assisting in the success of students on and off campus.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.075 | 0.047 |
| Scholarly communication | 0.035 | 0.012 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".