RUNNING HEAD: A CONTEXTUALIZED METHOD OF INQUIRY FOR UNDERSTANDING PERCEPTIONS OF MOBILE AND UBIQUITOUS COMPUTING TECHNOLOGIES Corresponding Author’s Contact Information:
Bibliographic record
Abstract
Her primary research interests are in human-computer interaction and ubiquitous computing, specifically in the areas of record-keeping and identity management systems and support for education and healthcare. Khai N. Truong is an Associate Professor in the Department of Computer Science at the University of Toronto. His research interests are in human-computer interaction and ubiquitous computing, specifically in the areas of assistive technologies, mobile interaction techniques, and sustainability.- 2-In this paper, we describe the origins, use, and efficacy of a contextualized method for evaluating mobile and ubiquitous computing systems. This technique, which we called “paratyping, ” is based on experience prototyping and event-contingent experience sampling, and allows researchers to survey people in real-life situations without the need for costly and sometimes untenable deployment evaluations. We used this tool to probe the perceptions of the conversation partners of users of the Personal Audio Loop, a memory aid with the potential for substantial privacy implications. Based on that experience, we refined and adapted the approach to evaluate SenseCam, a wearable
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".