Persuasive Technology : 14th International Conference, PERSUASIVE 2019 Limassol, Cyprus, April 9–11, 2019 Adjunct Proceedings
Bibliographic record
Abstract
Persuasive technology is a vibrant interdisciplinary research field, focusing on the study, design, development, and evaluation of information technologies aimed at influencing people’s attitudes or behaviors through open and transparent means. The International Conference on Persuasive Technology series brings together researchers, designers, practitioners and business people from various disciplines and a wide variety of application domains. The research community seeks to facilitate healthier lifestyles, make people feel or behave more safely, reduce consumption of renewable resources, among other notable goals, by, for instance, designing software applications, monitoring through sensor technologies, analyzing obtained data, and providing various types of coaching for users. The 14th International Conference on Persuasive Technology was hosted by the Department of Communication and Internet Studies at the Cyprus University of Technology in Limassol, Cyprus in April 2019, and organized in collaboration with the University of Oulu, Finland and the University of Wollongong, Australia. In previous years similar highly successful conferences were organized in Waterloo (Canada), Amsterdam (Netherlands), Salzburg (Austria), Chicago (United States), Padua (Italy), Sydney (Australia), Linköping (Sweden), Columbus (United States), Copenhagen (Denmark), Claremont (United States), Oulu (Finland), Palo Alto (United States), and Eindhoven (Netherlands). The conference addressed a wide variety of topics regarding the development of persuasive and behavior change support systems. This year papers were also solicited for two specific topics, namely Personal informatics and Gamification and gamified persuasive technologies.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.231 | 0.108 |
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".