Bibliographic record
Abstract
This page contains the i) SQLite database, and ii) scripts and instructions for the paper titled Opening the Valve on Pure-Data: Usage Patterns and Programming Practices of a Data-Flow Based Visual Programming Language. We have provided two main files in this link: dataset.tar.gz scripts_and_instructions.zip Additionally, the i) SQLite database, ii) scripts and instructions, and iii) mirrored repositories of the PD projects can also be found in the following link: https://archive.org/details/Opening_the_Valve_on_Pure_Data. The download instructions are as follows: Our dataset is available at this link and also at archive.org and at https://zenodo.org/records/10576757 as a file titled dataset.tar.gz (~1.12GB). You can download the file and then you can unzip the database by running tar -xzf dataset.tar.gz. You can also find the scripts and instructions needed to use our database and replicate our work inside the scripts_and_instructions.zip (~116MB) file, which you can download from this link and also from the same archive.org link. After that, you can unzip the scripts_and_instructions.zip file by using the command: unzip scripts_and_instructions.zip. Finally, the mirrored PD repositories are available at archive.org. The file is titled pd_mirrored.tar.gz (~242.5GB). You can download the zipped folder of the mirrored repositories using the following command: wget -c https://archive.org/download/Opening_the_Valve_on_Pure_Data/pd_mirrored.tar.gz. After that, you can unzip the file using tar -xzf pd_mirrored.tar.gz. You can find a README.md file inside the unzipped directory titled scripts_and_instructions detailing the structure and usage of our dataset, along with some sample SQL queries and additional helper scripts for the database. Furthermore, we have provided instructions for replicating our work in the same README file.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.091 |
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".