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
Digital data, from texts to files and mobile applications, has become a pervasive component of our society. With seemingly unlimited storage in the cloud at their disposal, how do people approach data preservation, deciding what to keep and discard? We interviewed 23 participants with diverse backgrounds, asking them about their perceived digital data: what "stuff" they kept through the years, why, how they used it, and what they considered important. In an iterative analysis process, we uncovered a spectrum of tendencies that drive preservation strategies, with two extremes: hoarding (where participants accumulated large amounts of data, even if considered of little value) and minimalism (where they kept as little as possible, regularly cleaning their data). We contrast and compare the two extremes of the spectrum, characterize their nuanced nature, and discuss how our categorization compares to previously reported behaviors such as filing and piling, email cleaners and keepers. We conclude with broad implications for shaping technology.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it