An Analysis of the Dynamics of the Legitimation Processes of Innovations in Open Source Software: A Qualitative Study of the Rational Deliberations in the Drupal Project
Bibliographic record
Abstract
Within the Open Source Software (OSS) literature, there is a lack of studies addressing the legitimation processes of innovations that are born in OSS. This study sets out to analyze the legitimation processes of innovations within the deliberations of the Drupal project. The data set constitutes 52 rational deliberation cases discussing innovations that were proposed by members of the community. Habermas’s Ideal Speech Situations (ISS) is used as the framework to view Drupal’s rational deliberations from; in fact within the 52 cases that are examined in this thesis, there were no violations to the guidelines of the ISS in the deliberations. The Communicative Action Theory, Influence Tactics theory and the theory of Validity Claims are aspects of the framework that is used to code and analyze the conversations. These aspects allow for an effective conceptualization of the dynamics of the Drupal deliberations. This thesis was able to find that legitimation processes of innovations in open source software were influenced by the type, complexity and implications of the innovations on the rest of the community. Also, bug fixes, complex innovations and innovations that have implications on the rest of the software will result in a long (in terms of number of comments) legitimation process. Also, it is empirically backed in this study that in open deliberations that aim at achieving mutual understanding towards a common goal, the communicative action type and the rational persuasion influence tactic are the most common methods for innovators to interact with the community.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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 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".