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
Purpose The aim is to present a novel approach for tackling multi‐attribute decision problems with using concepts from grey system theory and the extent analysis method. Design/methodology/approach Underlying judgment set of a multi‐attribute decision problem is modeled with employing grey numbers. Fundamental principles for comparing a set of grey numbers are established. A procedure for evaluating the decision alternatives is explained in detail and referred to as “The grey extent analysis”. Findings It is shown that the proposed procedure can be used as a decision‐making tool where the judgments of the decision maker are not exact (i.e. in terms of grey system terminology they are not “white”). Research limitations/implications The grey numbers utilized are assumed to be asynchronous and uniformly distributed over their domain. Originality/value The extent analysis is well studied as an evaluation tool under a fuzzy system. This study is set apart from the previous extent analysis research for two reasons: this paper provides basic guidelines for applying the extent analysis procedure within a grey system; the (grey) number comparison principles are totally different.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it