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.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.335 | 0.274 |
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".