Bibliographic record
Abstract
This study focuses on algorithmic interactions in open source work. Algorithms are essential in open source because they remedy concerns incompletely addressed by parallel development or modularity. Following algorithmic interactions in open source allows us to map the operational performance of algorithms to understand how algorithms work with multiple other algorithms to accomplish work. Studying algorithms working together shows us how residual interdependencies of modularity and problems not resolved by dependence on parallel development are worked around to perform open source work. We examine the Linux Kernel case that reveals how algorithmic interactions facilitate open source work through the three processes of managing, organizing, and supervising development work. Our qualitative study theorizes how algorithmic interactions intensify through these processes that work together to facilitate development. We make a theoretical contribution to open source scholarship by explaining how algorithmic interactions navigate across module rigidity and enhance parallel development. Our work also reveals how, in open source, developers work to automate most tasks and augmentation is a bidirectional relationship of algorithms augmenting the work of developers and of developers augmenting the work of algorithms.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".