Bibliographic record
Abstract
Personal pictures storage is currently split between a myriad of physical and digital tools. Cloud photo storage and social networks are seeing increasing adoption, and are being recommended to families (especially older generations) as digital pictures solutions. The ubiquity of these platforms raises the question of whether the design of their photo-based operations consider the mental models of their cross-generational users. Understanding mental models is a key factor for the usability (and adoption) of these technologies. Previous works have observed that perceptions of digital storage limit adoption, especially for older users. However, we do not yet understand users' mental models of these applications. This impedes efforts to design applications better matching diverse user needs. We present here a cross-generational investigation of users' mental models of ubiquitous picture technologies, including cloud storage and social sharing. We find that mental models are split (both between generations and domains), contributing to lower adoption by older adults. Our analysis reveals that digital tools need to understand their roots in physical pictures and bridge this divide by including physical concepts as an aspect of use, if we are to support cross-generational interactions with personal and family pictures.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.211 | 0.070 |
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".