Development of a Theory-grounded Socialization Framework to Investigate Newcomer Socialization in Free/Open Source Software Communities
Bibliographic record
Abstract
Attracting a large number of new contributors has been seen as a way to ensure the survival, long-term success, and sustainability of Free/Open Source Software (FOSS) communities. FOSS communities have thus for long seized the criticality of generating effective initiatives to facilitate the socialization of community newcomers. However, FOSS socialization research has suffered from a lack of well-grounded theoretical considerations. This research project uses the well-acknowledged socialization model from Van Maanen and Schein (1979) to revisit FOSS socialization by deriving a FOSSspecific socialization framework and its associated measurement instrument. The paper provides a theoretically-grounded and fully-validated research tool for researchers who wish to study the FOSS socialization phenomenon. The study reported on here used a three-phased approach involving the construction of a socialization framework using qualitative data gathering, the development of a measurement instrument, and its validation using a full-scale online survey involving 367 contributors from 12 large FOSS communities.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".