Do Subjectivity and Objectivity Always Agreeƒ A Case Study with Stack Overflow Questions
Bibliographic record
Abstract
In Stack Overflow (SO), the quality of posts (i.e., questions and answers) is subjectively evaluated by users through a voting mechanism. The net votes (upvotes − downvotes) obtained by a post are often considered an approximation of its quality. However, about half of the questions that received working solutions got more downvotes than upvotes. Furthermore, about 18% of the accepted answers (i.e., verified solutions) also do not score the maximum votes. All these counter-intuitive findings cast doubts on the reliability of the evaluation mechanism employed at SO. Moreover, many users raise concerns against the evaluation, especially downvotes to their posts. Therefore, rigorous verification of the subjective evaluation is highly warranted to ensure a non-biased and reliable quality assessment mechanism. In this paper, we compare the subjective assessment of questions with their objective assessment using 2.5 million questions and ten text analysis metrics. According to our investigation, four objective metrics agree with the subjective evaluation, two do not agree, one either agrees or disagrees, and the remaining three neither agree nor disagree with the subjective evaluation. We then develop machine learning models to classify the promoted and discouraged questions. Our models outperform the state-of-the-art models with a maximum of about 76%–87% accuracy.
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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 | 0.126 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".