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
In this paper, we offer an alternative vision for domain driven development (3D). Our approach is model driven and emphasizes the use of generic and specific domain oriented programming (DOP) languages. DOP uses strong specific languages, which directly incorporate domain abstractions, to allow knowledgeable end users to succinctly express their needs in the form of an application computation. Most domain driven development (3D) approaches and techniques are targeted at professional software engineers and computer scientists. We argue that DOP offers a promising alternative. Specifically we are focused on empowering application developers who have extensive domain knowledge as well as sound foundations in their professions, but may not be formally trained in computer science.We provide a brief survey of DOP experiences, which show that many of the best practices such as patterns, refactoring, and pair programming are naturally and ideally practiced in a Model Driven Development (MDD) setting. We compare and contrast our DOP with other popular approaches, most of which are deeply rooted in the OO community.Finally we highlight challenges and opportunities in the design and implementation of such languages.
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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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