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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".