Community‐driven user evaluation of the Inuvialuit cultural heritage digital library
Bibliographic record
Abstract
ABSTRACT Preservation of and access to aboriginal and indigenous cultural heritage is emerging as a key area of information science research and development. In this study, we report on a community‐driven user interface evaluation within a cultural heritage digital library that was developed for the Inuit communities in the Inuvialuit Settlement Region in Canada's north. The study adopted a culturally‐aware, multi‐method and multidisciplinary user evaluation framework to examine the usability and usefulness of the Digital Library North user interface. The three‐phase user evaluation study consisted of such methods and approaches as environmental scanning, surveys, interviews, information audits, information tables, open houses as well as various community workshops. The iterative nature of the digital library interface development and usability evaluation provided rich and varied feedback and suggestions for the improvement of the user interface and its searching, browsing and navigation functionalities. The three thematized categories that emerged from the diverse data collected over the course of the three phases include user engagement, search and browse, and interface features. The study found that the development of cultural heritage digital libraries and their user interfaces for northern communities requires diverse, multidisciplinary and longitudinal methodologies that ensure their success, and more importantly, their sustainability and acceptance.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".