Bibliographic record
Abstract
Summary form only given. Information granules and their computing, which give rise to the framework of Granular Computing, deliver interesting opportunities to endow processing with an important facet of human-centricity. This facet directly implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans and generates results being seamlessly comprehended by users. Given that systems, which are quite commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of our study. We elaborate on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, we concentrate on an idea of knowledge-based clustering, which aims at the seamless realization of the data-expertise design of information granules. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. The other interesting directions enhancing human centricity of computing with fuzzy sets, deals with non-numeric, semi-qualitative characterization of information granules (fuzzy sets) as well as inherent evolving capabilities of associated human-centric systems. We discuss a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulate a series of associated optimization tasks guided by well-formulated performance indexes, and discuss the underlying essence of the resulting solutions.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".