Bibliographic record
Abstract
Weights or weighted attributes are a part of most measurement, indexing and classification techniques.However, when judgments are subjective; weight assignment, and especially weight consistency, is almost always problematic.A ranking or preference is usually defined as a weakly ordered relationship between a set of items such that, for any two items, the first is either "less preferred", "more preferred" or "indifferent" to the second one.While most existing methods involve numbers, in many cases using only qualitative assessments might be more trustworthy.Formulas and rules involving numbers are considered more scientific and credible than those that involve qualitative values only.This is obviously true when the notions of interest can be measured directly or indirectly, as for instance velocity, height, voltage, pressure etc.However, when it comes to subjective notions as love, importance, taste, beauty, etc., we have to be very careful when numbers are used.One of the ways to deal with such intangible concepts is the pairwise comparisons method.This method is based on the observation that it is much easier to judge the mutual relationship (preference, importance, intensity, etc.) of two objects than to do this for several objects at once.This special issue of Fundamenta Informaticae is devoted to different aspects of the pairwise comparisons method.It is comprised of fourteen excellent articles that present the phenomenon of pairwise comparisons from various perspectives.The work, "Continuous Pairwise Comparisons" written by Thomas Saaty definitely goes far beyond currently ongoing discussions and opens up new horizons for researchers.In the article he proposes changing perspective from a discrete to a continuous one.The suggested solution is to determine the rankings for continuous pairwise comparisons based on solving Fredholm's integral equation of the second kind.In "Complex Ranking Procedures" the authors Barbara Sandrasagra and Michael Soltys investigate pairwise ranking problems where relatively few items are to be ranked with a complex procedure and according to a large number of criteria.They discuss their solutions in the context of tender procedures.Andrew Schumann and Jan Woleński enrich the discussion on pairwise comparisons methods by presenting their logical approach enclosed in the article "Two Squares of Oppositions and Their Applications in Pairwise Comparisons Analysis".
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.553 | 0.389 |
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".