What Can OSS Mailing Lists Tell Us? A Preliminary Psychometric Text Analysis of the Apache Developer Mailing List
Bibliographic record
Abstract
Developer mailing lists are a rich source of information about Open Source Software (OSS) development. The unstructured nature of email makes extracting information difficult. We use a psychometrically-based linguistic analysis tool, the LIWC, to examine the Apache httpd server developer mailing list. We conduct three preliminary experiments to assess the appropriateness of this tool for information extraction from mailing lists. First, using LIWC dimensions that are correlated with the big five personality traits, we assess the personality of four top developers against a baseline for the entire mailing list. The two developers that were responsible for the major Apache releases had similar personalities. Their personalities were different from the baseline and the other developers. Second, the first and last 50 emails for two top developers who have left the project are examined. The analysis shows promise in understanding why developers join and leave a project. Third, we examine word usage on the mailing list for two major Apache releases. The differences may reflect the relative success of each release.
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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 | 0.009 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".