Integrating Individual and Social Contexts into Self-Reflection Technologies
Bibliographic record
Abstract
Technology for promoting reflection can help people better understand their emotions and thought patterns, eventually motivating them to take action to adopt healthy or productive behaviors. However, existing work has often viewed users individualistically, addressing people’s behaviors and emotions rather than recognizing the external factors that shape them (e.g., economic status, culture). We envision that the individualistic approaches can be extended and reimagined in ways that can consider such broader contexts. We believe such a shift in the design of interventions will help individuals reflect in a more holistic manner, supporting collaborative reflection processes that involve more than one person. With these aims in mind, we will discuss the two questions in our workshop. First, what individual and social contexts should HCI researchers consider while promoting reflection? Second, what role can various forms of technology (e.g., just-in-time adaptive interventions, peer-support platforms) play in supporting and augmenting reflective practices? Through our workshop, we hope to bring together a community of multidisciplinary researchers and practitioners who aim to design and develop reflection interventions that are situated within the fabric of users’ individual and social contexts.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".