Replication package for "Analyzing techniques for duplicate detection on Q&A websites for game development"
Bibliographic record
Abstract
<strong>Replication package for the paper "Analyzing techniques for duplicate detection on Q&A websites for game development"</strong> The data contained in this package includes everything we used in the study, including all of the results, measures, and the models trained throughout the methodology. The code used in the paper can be found in its GitHub repository. More information about how the data is organized can be found in the README files in that repository. All the data was collected from the June 2021 Stack Exchange Data Dump. <strong>Benchmark datasets</strong> We provide three datasets that can be used for evaluating other duplicate detection methodologies and comparing with our results. There are two datasets focused on game development based on questions from Stack Overflow and the Game Development Stack Exchange. The third dataset is comprised of five randomly selected samples of equal size collected from Stack Overflow. All of these datasets were extracted from the June 2021 Stack Exchange Data Dump. More information about the datasets is available in our paper.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".