Three shots are better than one
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.106 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it