Implementing CASE tools: the need for a normative framework
Bibliographic record
Abstract
Procedures and standards that are used to integrate methods, techniques, and tools for the development and maintenance of information systems are described. These procedures and standards are considered to be a normative framework. Such a framework is necessary when organizations implement computer-aided software engineering (CASE) tools. The concept of the normative framework is explained, and its implementation by a major financial institution is described. The reasons for the decision to develop a normative framework are discussed, as well as the nature and contents of the framework being developed. Observations made during its implementation are presented.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.240 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.043 | 0.059 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".