About Business Decision Making by A Consistency-Driven Pairwise Comparisons Method
Bibliographic record
Abstract
Writing this paper has been inspired by the most recent economic crises in the world. Better decision making methods are more needed now than ever before. This study presents an innovate approach to the assessment of management capability in businesses. It is based on the consistency-driven pairwise comparisons method. A proposed conceptual model of performance is flexible and adaptable to different requirements and preconditions (e.g., grant or loan applications). Considering the complexity, a hierarchical structure is used and an inconsistency analysis is performed for all the levels of the structure. The pairwise comparisons method synthesizes together performance assessments assessed at two levels (in our case; there may be more levels in general). The method of consistency-driven pairwise comparisons can be combined with other quantitative and qualitative assessment methods (including brainstorming and Delphi method). Non measurable criteria which often bypassed in other approaches, can be included in the presented model. The consistency-driven pairwise comparisons method contributes to the reliability of assessment through the consistency analysis and solid statistical studies show the accuracy improvement. The management capability merit index (MC-merit) and a procedure for computing it are introduced.
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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 | 0.132 | 0.199 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".