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
Software engineering is still a young discipline. Software development group managers must keep their groups current with this dynamic body of knowledge as it evolves. There are two basic approaches: require staff to have both application expertise and software expertise, or create a software cell. The latter approach runs the risk of two communities not communicating well, although it might make staying abreast of changes in software engineering easier. The first approach should work better than it does today if some new educational patterns are put in place. For example, we could start treating software more like mathematics, introducing more software courses into undergraduate programs in other disciplines. Managers must also focus on the best way to develop software expertise for existing staff. Staff returning to school for a master's in software engineering can acquire a broad understanding of the field, but at a substantial cost in both time and effort. Short courses call help to fill this gap, but most short courses are skill based, whereas a deeper kind of learning is needed. As the first step, however, managers must assess software's impact on their bottom line deliverables. It might surprise them how much they depend on software expertise to deliver their products.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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