Bibliographic record
Abstract
This position paper addresses the fourth issue submitted to the panel members: the identification of roles for national and international bodies and what actions should they undertake to advance "best practices" in software engineering. In the fifty-odd-year history of software, various methods and techniques, methodologies and tools have been proposed to facilitate the development and maintenance of software responsive to needs. Most have proved to be more specific to the then current state of technology than was understood at the time. As a result, many have subsequently been shown to be less universally applicable than originally intended. Despite a plethora of conferences and workshops over recent decades, and numerous periodicals, books and courses, software engineering continues to lack a set of universally recognised fundamental principles. Indeed, relatively few researchers have pursued this important subject in comparison with other research tracks in software engineering.
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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 | 0.077 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".