Editorial: A special issue dedicated to Franco Giannessi
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
It is our immense pleasure to dedicate this special issue to Professor Franco Giannessi on the occasion of his 85th birthday. Prof. Giannessi's profound and original ideas influenced and shaped various research directions in variational analysis and mathematical optimization over many decades. He pioneered the notion of vector variational inequalities, which is now an important branch of applied mathematics. He independently discovered the powerful mathematical tool called the image space approach, strengthened it, and addressed crucial applications employing it. There are essential results available in optimization, control, equilibrium problems, and variational and quasi-variational inequalities that have been derived by using the mechanism of the image space analysis. He also proposed the notion of the now so-called G-semi-differentiability of a function. This special issue aims to acknowledge and celebrate his beautiful ideas and novel contributions as an innovative researcher of the highest caliber.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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