What is a Library Website, Anyway? Reconsidering Dominant Conceptual Models
Bibliographic record
Abstract
In late 2019, Thompson Rivers University embarked on a multi-phase website usability project beginning with a website user survey, to be followed shortly afterward by usability testing and interviews. While the survey was completed as planned, the COVID-19 pandemic closed the library and interrupted the usability testing phase. This interruption and the frantic website changes that followed led me to consider survey findings within the context of differing conceptual models of the library website as a whole. This study explores a number of conceptual models of the library website in further depth, considering evidence from both the existing literature and the user survey in addition to the researcher’s own experience making post-COVID website updates. Particular models that are examined include Website as Research Portal, Website as Extension or Representation of the physical library, and Website as Library Branch. Each of these conceptual models has different implications on priorities, structure, purpose, and resource allocation. Rather than considering the models of library employees superior or more advanced than those of students, I contend that an awareness of myriad ways to understand the website can best bridge the gap between library employees and other users. The study concludes that while there is no perfect model of the library website, considering and communicating our models may sharpen collegial decision-making structures and create greater unity of purpose within the library.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.047 | 0.057 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| 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".