Seeing Double at Memorial University: Two WorldCat Local Usability Studies
Bibliographic record
Abstract
Obtaining user feedback is valuable yet often overlooked, so when Memorial University Libraries launched OCLC’s WorldCat Local discovery tool for Fall 2010, usability testing was a logical next step. Two branches, each with distinct user groups, the Ferriss Hodgett Library in Corner Brook, and the Health Sciences Library in St. John’s, conducted unique usability studies to gain evidence into how WorldCat Local performed common research tasks. The Health Sciences Library was inspired by the information seeking habits of Memorial University’s Faculty of Medicine, School of Nursing, and School of Pharmacy. This demographic’s heavy reliance on journal literature, and known item searching made these users an interesting test group. The Ferriss Hodgett Library, serving an undergraduate Liberal Arts and Sciences population, was interested in the information seeking behavior of new students with little to no experience using library resources. At the local level, evidence obtained as a result of this usability testing will provide guidance into future use of WCL at Memorial University Libraries. On a broader scale, the usability findings are relevant to any library considering the shift to a new discovery tool.
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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 | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".