Bibliographic record
Abstract
For the most part, information landscapes such as libraries are structured, organized, created, and used by the dominant groups. These spaces may be unfamiliar territory for many students. Humour used in library orientation elicits enjoyment and helps to connect librarians and students. Low and high inference humour used during orientation can help connect students new to those landscapes with information and to librarians. Appropriate use of instructional humour in orientations can reduce students’ anxiety about using the library, especially when they need help from library staff. This reflective write up on using humour in library orientations, is to demonstrate how we used humour to create a comfortable learning environment, to encourage students to visit the library, to improve (hopefully!) recall and retention of course content, and enable positive associations with library resources or the librarian. There are challenges with humour when the classroom is diverse or if humour is used negatively. Care should be given to use humour to support course content.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.241 | 0.182 |
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".