Bibliographic record
Abstract
Academic libraries deliver library instruction, but how good are practitioners at measuring the effectiveness of their efforts? One medium-sized Canadian university library undertook a new approach to assessing its library instruction programme by collaborating with faculty members and engaging with their course content. Looking initially at recently-offered information literacy (IL) sessions, the study challenged commonly-held assumptions on the programme, and established a number of broad conclusions. All faculty members from two disciplines were invited to submit syllabi for courses taught in the past few years. In addition to those courses that regularly scheduled sessions in the library, the authors received course content from instructors that had not traditionally booked library instruction, providing a unique opportunity for analysis and to learn about research content in the course, requirements of independent use of the library, inclusion of standards on academic integrity, inclusion of a cumulative project, the presence of library instruction, critical thinking, library assignments, general reference to the library and its resources, and whether professors conduct library-type instruction.
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.
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 | 0.022 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.114 | 0.030 |
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".