Split Decisions: Explicit Contexts for Substructural Languages (artifact)
Bibliographic record
Abstract
This artifact contains the Beluga mechanization accompanying the paper “Split Decisions: Explicit Contexts for Substructural Languages” (CPP '25). The artifact contains the following files: carve.tar: An archive of all source files related to mechanization, along with documentation cpp-carve.ova: A Virtual Machine image with all dependencies required to run the mechanization, including the code itself To run the mechanization directly, download and extract carve.tar. This will create a folder named carve; from there, the file carve/README.md contains detailed instructions for installing the proof assistant Beluga and running the mechanization. To run the Virtual Machine set-up, download the .ova file and import as necessary. We recommend using VirtualBox; one can import the Virtual Machine directly from the cpp-carve.ova file by following instructions at this link. Adjust the CPU/RAM allocation as needed; a configuration of 4 cores with 4 GB of RAM should suffice. If using other Virtual Machine software, you may need to extract the .ova file to obtain an .ovf file and follow the appropriate instructions. The Virtual Machine instance is a Ubuntu Server with an SSH server, containing all relevant dependencies and carve pre-installed. The username and password are both cpp25. After successfully launching and logging in (via terminal or SSH), run the following: cd carvebeluga run_all.cfg
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.040 |
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".