The Journal of Object Technology
Bibliographic record
Abstract
The 12th International Conference on Model Transformations ICMT 2019 aims at advancing the state of knowledge about model transformation technologies.Model transformations are a core technology underlying the use of modeling and Domain Specific Languages (DLSs).Especially in DSL development, model transformations enable a decoupling of abstract and concrete syntax(es), supporting reuse and the coexistence of multiple concrete syntaxes (visual, textual) for the same DSL.Transformations also play a key role in analyzing models to reveal conceptual flaws or highlight quality bottlenecks, and in integrating heterogeneous tools into unified tool chains.ICMT 2019 received 15 submissions and 6 papers were accepted, resulting in an acceptance rate of 40%.ICMT 2019 includes an excellent keynote given by Paul Klint (CWI Research Fellow, University of Amsterdam, The Netherlands) on DSLs, the Good the Bad and the Ugly.ICMT 2019 will also feature a panel Is there a Future for Model Transformation Languages?that takes a critical look at the traditional approach(es) to designing model transformation languages and poses the question if these might become obsolete and be replaced with, e.g., approaches based on learning model transformations from examples.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.047 |
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".