Student Use of the Information Commons: An Exploration through Mixed Methods
Bibliographic record
Abstract
Abstract Objective – In this case study, librarians at the William H. Hannon Library at Loyola Marymount University explored user behaviour in the Information Commons, user preferences for furniture style and configuration, and how users engaged with a mix of technology, resources, and activities inside the space. Methods – The researchers used a mixed-methods case study consisting of 2,443 “direct observations,” 646 environmental scans, 248 patron surveys, and 46 whiteboard poll questions. They created visualizations of results in Tableau, with filters for zone and variable. They then carried out a follow-up furniture preferences survey with 190 respondents. Results – Independent study dominated the space usage. Users valued spaciousness, quiet, privacy, and a clean environment. Users frequently multi-tasked with additional devices as they simultaneously used the library computers, including cell phones, headphones, and laptops. The majority of students self-reported using a library computer for email and to access the campus online learning platform. They also reported reading/studying and printing as frequent activities, although these were less frequently observed. Unattended belongings were observed along with broken electrical outlets. Temperature and noise levels were highly variable. Conclusions – This methodology allowed for the exploration of space use and satisfaction and uncovered implications for the redesign of the library space. The library has already taken steps toward making improvements based on this assessment project including: removing some reference stacks in favor of additional seating space, an inventory of all electrical outlets, and the exploration of new furniture and noise control strategies.
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 | 0.073 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".