Convergence of Software Science and Computational Intelligence
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 Science is a discipline that studies the theoretical framework of software as instructive and behavioral information, which can be embodied and executed by generic computers in order to create expected system behaviors and machine intelligence. Intelligence science is a discipline that studies the mechanisms and theories of abstract intelligence and its paradigms such as natural, artificial, machinable, and computational intelligence. The convergence of software and intelligent sciences forms the transdisciplinary field of computational intelligence, which provides a coherent set of fundamental theories, contemporary denotational mathematics, and engineering applications. This editorial addresses the objectives of the International Journal of Software Science and Computational Intelligence (IJSSCI), and explores the domain of the emerging discipline. The historical evolvement of software and intelligence sciences and their theoretical foundations are elucidated. The coverage of this inaugural issue and recent advances in software and intelligence sciences are reviewed. This editorial demonstrates that the investigation into software and intelligence sciences will result in fundamental findings toward the development of future generation computing theories, methodologies, and technologies, as well as novel mathematical structures.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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