Bibliographic record
Abstract
Languages and systems to support generative and transformational solutions have been around a long time. Systems such as XVCL, DMS, ASF+SDF, Stratego and TXL have proven mature, efficient and effective in a wide range of applications. Even so, adoption remains a serious issue - almost all successful production applications of these systems in practice either involve help from the original authors or years of experience to get rolling. While work on accessibility is active, with efforts such as ETXL, Stratego XT, Rascal and Colm, the fundamental big step remains - it's not obvious how to apply a general purpose transformational system to any given generation or transformation problem, and the real power is in the paradigms of use, not the languages themselves. In this talk I will propose an agenda for addressing this problem by taking our own advice - designing and implementing domain specific languages (DSLs) for specific generative, transformational and analysis problem domains. We widely advise end users of the need for DSLs for their kinds of problems - why not for our kinds? And we use our tools for implementing their DSLs - why not our own? I will outline a general method for using transformational techniques to implement transformational and generative DSLs, and review applications of the method to implementing example text-based DSLs for model-based code generation and static code analysis. Finally, I will outline some first steps in implementing model transformation DSLs using the same idea - retaining the maturity and efficiency of our existing tools while bringing them to the masses by "eating our own dogfood".
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.207 | 0.098 |
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".