Conceptualizing User Satisfaction in the Ubiquitous Computing Era
Bibliographic record
Abstract
In this research-in-progress paper we argue that technology in the ubiquitous computing era offers experiences to users that extend well beyond the functional, practical applications offered in the world of work. In this era a realm of engagement is opening up to the individual that transcends the utilitarian, to encompass hedonic and social existence. Our central argument, therefore, is that user satisfaction is a notion which must extend to encompass rich, holistic human experience involving complex and fleeting interactions, driven by highly personal circumstances. We argue that the expectations, requirements and value perceptions of individuals in this dynamic context may only be anticipated and understood if situational factors (such as location, time, context, history-of-use) and quality of life factors (such as life stage, mobility, health, income, background, education) are taken into account. We identify the fundamental differences in key characteristics of user satisfaction between the traditional and ubiquitous computing environments and provide details about our own research approach, in which we are exploring ubiquitous content provision from the perspective of content providers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".