Three shots are better than one
Bibliographic record
Abstract
In an attempt to expand Information Literacy (IL) instruction beyond the one-shot, the Thompson Rivers University (TRU) Library established the English Library Instruction Pilot (ELIP) in 2023-2024. Students involved in the project participated in a series of three tutorials. The outcomes of the tutorials were aligned to both their Introduction to Academic Writing (English 1100) class and the ACRL Framework for Information Literacy. In experimenting with the new model, we asked the following questions: Did the ELIP programme help students succeed in their associated English 1100 courses? Does more integrated instruction aid in relationship-building between the library and the TRU community? How can we improve our instruction practices to better meet student needs? This paper discusses the formation of the programme, the results from our evaluation of it, and reflects on future directions and improvements. Through an examination of student assignments, a faculty feedback survey, and reflective journaling of librarian instructors, we conclude that the programme helped students complete the outcomes of their associated English 1100 class. It also contributed to relationship-building between the library and the university community and helped significantly improve existing teaching practices and materials in the library. The ELIP programme is unique in its departure from both the one-shot and credit course IL models, and we hope that our reflections will encourage other librarians to reflect and experiment with their instructional spaces.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.099 | 0.025 |
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".