Editorial: Enterprise Participation (January 2009)
Bibliographic record
Abstract
In The Role of Participation Architecture in Growing Sponsored Open Source Communities, Joel West and Siobhan O'Mahony argue that "to some extent, firms and technical communities have always collaborated to create standards, shared infrastructure, and innovation outcomes that are bigger than any one firm can achieve." and that "there is increasing evidence that path breaking innovations cannot occur without a community to interpret, support, extend and diffuse them". When considered in this light, it should not be surprising that more enterprises, both large and small, are increasing their participation in open source communities to drive innovation. The theme for this month's issue of the OSBR is enterprise participation and the authors provide practical advice for effective enterprise/community collaboration. Their experiences provide perspectives on: i) the Eclipse Foundation, which maintains an ecosystem of over 150 enterprises that participate in Eclipse open source projects; ii) an independent software vendor that sells closed source solutions constructed on top of an open source platform to large enterprise customers; iii) the impact of major players collaborating on a common open source platform for the mobile industry; iv) the role users can play in the very large (over 14 million) GNOME community; and v) the lessons a scientist from the National Research Council of Canada learned when he released software and started a small open source community.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".