Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the beginning of computer science more than 50 years ago, software engineers have sought techniques resulting in higher levels of quality and productivity. Some of these efforts have concentrated in increasing the level of abstraction in programming languages (from assembler to structured languages to object-oriented languages). In the last few years, we have witnessed an increasing focus on development based on high-level, graphical models. They are used not only as a means to documentthe analysis and design activities, but also as the actual “implementation” of the application, as well as for automatic analysis, code, and test case generation. The notations used to describe the models can be standard and general purpose (for example, UML) or tightly customized for the application domain. Code generation for the full application is only accomplished for specific, well-understood application domains. A key initiative in this direction is OMG’s Model-Driven Architecture (MDA), where models are progressively transformed until executable code is obtained. In this chapter, we give an overview of these technologies and propose ideas following this line (concerning metamodeling and the use of visual languages for the specification of model transformation, model simulation, analysis and code generation), and examine the impact of model-based techniques in the development process.Request access from your librarian to read this chapter's full text.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it