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
Many design concepts can be expressed only indirectly in source code. When this occurs, a single concept at design results in a verbose amount of code that is scattered across the system structure. In this paper, we present explicit programming, an approach that enables a developer to introduce new vocabulary into the source to capture a design concept explicitly. An introduced vocabulary item modularizes the implementation details associated with a design concept, reducing the scattering of code needed to express the concept. The vocabulary item appears in the code where the concept is needed; uses of the vocabulary may thus remain distributed through the code. We believe explicit programming provides a useful engineering point, balancing modularization and separation in (at least) two cases. First, when a design concept is tightly coupled with particular constructs in a program, separation is unlikely to lead to any benefits of reusability or comprehensibility. Second, concepts that emerge as a system evolves can be encapsulated and recorded, paving the way for later separation when conditions warrant it. We introduce ELIDE, a tool that supports explicit programming in Java, and describe several cases showing the utility of the explicit programming approach.
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 | 0.001 |
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