Replication package for "An Empirical Study of Q&A Websites for Game Developers"
Bibliographic record
Abstract
Replication package for the paper "An Empirical Study of Q&A Websites for Game Developers" This repository contains the datasets and scripts used to replicate the results from the paper "An Empirical Study of Q&A Websites for Game Developers". This is an exact copy of the repository on GitHub: https://github.com/asgaardlab/done-21-arthur-gamedev_qa_websites-code Replication data The datasets used to replicate the results for the paper can be found in the data directory (data/). These are the datasets we obtained after running all of the notebooks in this repository. Two of the studied websites are owned by companies (Epic and Unity) and we are not legally allowed to share the textual contents of the questions and answers as they are considered intellectual property. Therefore, instead of sharing the content of those posts, we included the URLs to all of the pages where the information used in the paper can be found, so that they can be crawled by future researchers. This is not an issue for Stack Overflow and the Game Development Stack Exchange, since that data is provided by Stack Exchange in the Stack Exchange Data Dump (https://archive.org/details/stackexchange). Survey data: Unfortunately, our University's ethics board only allows us to share the survey responses in aggregated format, which is done in the paper. In this repository, we added the list of communities in which we shared the survey (data/surveyed_communities.csv). Using this repository If you are using the datasets provided in this repository, you just need to run the analysis notebook (code/analysis/paper_results.ipynb) to obtain the results as shown in the paper. Otherwise, if you want to run the whole pipeline from scratch, follow these steps: 1. Download the data from Unity Answers and the UE4 AnswerHub from their websites (you can use the URLs provided in our datasets). Parse the HTML pages and extract the required information. 2. Download the data from Stack Overflow and the Game Development Stack Exchange from the Stack Exchange Data Dump (https://archive.org/details/stackexchange). Run the notebooks to process the XML files from the Stack Exchange data dump (code/process_xml). For Stack Overflow, run the select_gamedev_posts.ipynb (code/process_xml/stackoverflow/select_gamedev_posts.ipynb) first. 3. Run the text processing notebook (code/text_processing.ipynb). 4. Run the topic modelling notebook (code/topic_modelling.ipynb). 5. Run the topic comparisons notebook (code/topic_comparisons.ipynb). 6. Finally, run the analysis notebook (code/analysis/paper_results.ipynb) to get the results as shown on the paper.
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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 | 0.075 | 0.342 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.322 | 0.114 |
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".