Cross‐cultural issues in user learning and the design of digital interfaces
Bibliographic record
Abstract
Abstract Hall (1976) believes that culture is a selective screen through which we see the world and that the basic differences in the way members from different cultures perceive reality are responsible for the mis‐communications of the most fundamental kind. Hofstede (1997) notes that cultural orientations are deeply embedded in cultures over hundreds and thousands of years and modern media have not dislodged these cultural orientations. Indeed, cultural norms, assumptions, values, and orientations remain crucial for understanding people from various cultures. This understanding extends to designing a variety of information retrieval systems for international access and use, including Web‐based digital libraries. Since the Web is international in nature, Web design should embed ‘cultural attractors” (e.g., colors, metaphors, language cues, navigation controls, and other visual elements) that should create the’ look and feel” to match the cultural expectations of the users of a local culture (Smith, et al., 2004). The speakers will address the various roles culture plays in the design and use of system interfaces, in general, and of digital libraries in particular. They will present analyses from their current research findings on culture and its impact on information seeking, interface design, and digital library development. They will discuss methods of bridging the gap between various cultures through both providing effective user‐centered system design and educating information professionals.
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 | 0.045 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".