Information Literacy in undergraduate research: transforming an old idea into a new environment.
Bibliographic record
Abstract
Elizabeth Braaksma, Vera Armann-Keown, and Michele Piercey-Normore.\nUndergraduate research has historically been an integral component of the educational experience in the Faculty of Science at the University of Manitoba, but rapidly changing science-related disciplines pose unique challenges to identify, evaluate, acquire, and use information. Students are required to demonstrate competency in research papers and conduct laboratory research. Information is scrutinized by anonymous reviewers and is exposed to ethical and legal ramifications. A model was developed to implement information literacy (IL) into existing programs that already have a strong research foundation using the ACRL (Association of College and Research Libraries) standards for Science and Engineering. The model aligns both sets of learning outcomes (information literacy and discipline-specific), and provides sample exercises with rubrics for evaluation. The integration of IL learning outcomes within the context of a discipline in which the student has an interest, enables a more powerful learning environment than if the outcomes were separated. The program ensures the five IL competency standards are met at each level of a four-year degree, and that students take responsibility for their own success resulting in greater retention throughout their programs and into their future careers. The implications are that the IL integration will provide the tools necessary to help students remain within and successfully complete their academic programs; and gain added value to their knowledge and skills that can be extended into society or to a graduate degree and beyond.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".