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
Volunteer computing is a powerful way to harness distributed resources to perform large-scale tasks, similarly to other types of community-based initiatives. Volunteer computing is based on two pillars: the first is computational - allocating and managing large computing tasks; the second is participative - making large numbers of individuals volunteer their computer resources to a project. While the computational aspects of volunteer computing received much research attention, the participative aspect remains largely unexplored. In this study we aim to address this gap: by drawing on social psychology and online communities research, we develop and test a three-dimensional model of the factors determining volunteer computing users' contribution. We investigate one of the largest volunteer computing projects - [email protected] - by linking survey data about contributors' motivations to their activity logs. Our findings highlight the differences between volunteer computing and other forms of community-based projects, and reveal the intricate relationship between individual motivations, social affiliation, tenure in the project, and resource contribution. Implications for research and practice are discussed.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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