Bibliographic record
Abstract
Many academic libraries conduct extensive user studies when redesigning their websites, considering characteristics such as design features, information architecture, and link and information placement. One of the less studied aspects impacting library website usability is choice of language. This article presents the results of a usability study conducted at a small Canadian academic library that assessed the impact of word choice on user interactions with its library website. The author provides an overview of the relevant literature and explores the role that word choice, especially on a library website’s home page, can play in user experience. Plusieurs bibliothèques universitaires effectuent des études d’utilisabilité lors des refontes de sites Web pour tenir compte de caractéristiques telles les particularités techniques, l’architecture de l’information ainsi que le placement des liens et de l’information. L’un des aspects les moins étudiés concernant l’utilisabilité d’un site Web de bibliothèque est le choix du langage. Cet article présente les résultats d’une étude d’utilisabilité effectuée à une petite bibliothèque universitaire canadienne qui a tenté d’évaluer l’impact du choix des mots et les interactions des usagers avec son site Web. L’auteur fournit un survol de la littérature pertinente et explore quel rôle le choix des mots, surtout sur la page d’accueil d’un site Web de bibliothèque, peut avoir sur l’expériences des usagers.
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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".