Open Source in Higher Education: Towards an Understanding of Networked Universities
Bibliographic record
Abstract
This article addresses the question of understanding more about networked universities by looking at open source software developers working in academic contexts. It sketches their identities and work as an emerging professional community that both relies upon and develops digitally mediated networks and contributes to the progress of academic knowledge and scientific discoveries. The data for this study revolve around questionnaire responses from developers working on two open source software projects, one from the field of biology, the other from education. It provides a rare glimpse into a highly specialised social world. The study demonstrates how the commitments and ideals of open source software developers not only contribute to universities as digitally network institutions, but are foundational to their very constitution.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.015 | 0.034 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".