Toward a Theory of Socialization in Open Source Software Communities: A Symbolic Interactionist Perspective
Bibliographic record
Abstract
Recent literature reviews highlight a gap in our understanding of how members socialize into virtual communities. Motivated by this shortcoming, we seek to understand how members socialize into open source software (OSS) communities, which are both virtual and normative in nature. Using the symbolic interactionist perspective, we define socialization and explicate the socialization process in OSS communities. Specifically, we define socialization as the extent of correspondence between the personal meanings of individual members and the shared meanings of the community, which emerge from the negotiated experiences of members. In addition, we posit that the socialization process consists of the interplay between individuals’ meaning-making process, consisting of both action and personal meaning, and the negotiated shared meanings within 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".