Bibliographic record
Abstract
<strong>Spark Empirical</strong> <br> This dataset contains Stack Overflow manual study results for the paper "An Empirical Study on the Challenges that Developers Encounter When Developing Apache Spark Applications". the <em>data</em> folder contains the <em>Stackoverflow Manual Results.csv </em>file that is the manual analysis result for the Stack Overflow posts. The CSV file contains information on the classification of the data, the reasons and the number of views, etc. the <em>scripts</em> folder contains the python and SQL files that are used for data collection and data analysis. <em>query_data.sql</em> is used to collect data from the Stack Exchange website. <em>sample.py</em> is used to sample data for the manual analysis in the paper. <em>common_issue.py</em> is used to study the percentage of common issues in rq1. <em>popularity.py </em>is used to calculate the average of normalized view counts in rq2. <em>popularity_difficulty.py</em> is used to calculate the average of raw view counts and the median hours to receive an answer in rq2. <em>root_cuase.py</em> is used to study the percentage of root causes in rq3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.569 | 0.015 |
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; both teacher heads agree on what is shown here.
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".