MACBETH (Working Paper LSEOR 03.56, London School of Economics, Londres)
Bibliographic record
Abstract
This paper presents an up-to-date comprehensive overview of MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). MACBETH is a multicriteria decision analysis approach that requires only qualitative judgements about differences of value to help a decision maker, or a decision-advising group, quantify the relative attractiveness of options. The approach, based on the additive value model, aims to support interactive learning about the evaluation problem and the elaboration of recommendations to prioritise and select options in individual or group decision making processes. We revise in detail the theoretical foundations of MACBETH and present a simple example that illustrates the use of the M-MACBETH decision support system (www.m-macbeth.com). It permits the structuring of value trees, the construction of criteria descriptors, the scoring of options against criteria, the development of value functions, the weighting of criteria, and extensive sensitivity and robustness analyses about the relative and intrinsic value of options. Reference is also made to some successful real-world consulting applications and a historical survey is also included, which describes the key stages, with their corresponding publications, in the development of MACBETH since early the 1990's.
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How this classification was reachedexpand
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; both teacher heads agree on what is shown here.
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".