Bibliographic record
Abstract
People with Repetitive Strain Injury (RSI) performing computer work for more than 4-5 hours/day are recommended to take microbreaks (30 sec to 1 min) every hour to reduce their symptoms.This is difficult during work as other things occupy their mind.While notifications can be used as reminders, they adversely impact the affective state and productivity of the user.Shape-changing devices demonstrate potential as they can provide passive awareness.We conducted an ideation session with HCI professionals to identify opportunities for shape-changing break reminders and interactive sessions with people with RSI.We found that the participants struggled to take enough breaks, found notifications inadequate, and modified their primary task to incorporate movement.They demonstrated an aversion to disruption, were receptive to shape-changing break reminders, and desired to emotionally engage with them.This demonstrates the potential of shape-changing break reminders as can be ambient and engender emotions through physical transformation.I want to start with thanking my wonderful supervisor, Dr. Audrey Girouard for giving me the opportunity to pursue my Master's degree under her guidance, for always being patient and supportive, and for creating an environment of constructive feedback and support in the lab
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".