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
Abstract Reuse of products, processes, and experience originating from the system life cycle is seen today as a feasible solution to the problem of developing higher quality systems at a lower cost. In fact, quality improvement is very often achieved by repeatedly reusing and modifying the same elements, learning about them by direct experience. This article presents an infrastructure, called the experience factory , aimed at capitalization and reuse of life‐cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from those of the development organization. The activities of the development organization and of the experience factory can be summarized as follows: The development organization develops and delivers systems with the aid of analyzed, synthesized, and packaged experiences from the experience factory. It provides the experience factory with raw project information such as developmental and environmental characteristics, product parts, processes, and resource and defect data, representing the project being developed. The experience factory supports project developments with direct feedback by analyzing and synthesizing all kinds of experiences gathered from projects as well as other state‐of‐the‐practice notions and acting as a repository for such experiences. These experiences include locally calibrated cost estimation models, processes demonstrated effective for the development environment, relevant products and product parts, and quality models.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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