Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dramatic advances in sensor and computing miniaturization for personal data collection are making Personal Informatics (PI) tools a reality. Yet, advances in data collection have not been matched with similar advances in tools to promote, support, and facilitate reflection on this data. This gap leaves people with large swaths of data, but very little understanding of how to make sense of the data or to derive actionable insights. In this work, we explore a process called shared reflection, where individuals are paired with other data collectors, and asked (through prompts) to reflect on one another?s data. Based on a six-week study where 15 participants collected different kinds of personal data and engaged in a shared reflection process, we show that participants gained transformative insights from others' reflections on their data. While this was promising, we discuss practical challenges in deploying this idea into real world personal informatics tools. In particular, while shared reflection can be appropriated to effectively bootstrap reflection on one's data, this needs to be balanced against privacy and control concerns.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it