M-MACBETH for Multicriteria Resource Allocation
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
The M-MACBETH DSS (www.m-macbeth.com) implements the MACBETH approach to evaluate projects on multiple criteria base only on qualitative pairwise comparison judgements about difference of attractiveness. This multicriteria decision aid tool supports the selection of a good/best project. However, in a context of scarce resources, choosing a portfolio of projects is a more demanding problem, as it requires not only to balance benefits against costs and the risks of realising the benefits, but also to evaluate several projects together. There are several DSS for multicriteria portfolio analysis, that differ on the resource allocation procedure used: prioritizing projects by decreasing values of benefit-to-cost ratios or identifying the optimal portfolio by mathematical programming. It is well-known that the portfolios arising from the approaches do not always coincide, therefore it would be useful to combine both approaches, but few DSS do so. Within this framework, a new resource allocation component of the M-MACBETH DSS was developed, which implements the two approaches interactively. One distinctive feature is the ability to explicitly address the baseline problem, by sensitivity analysis of the stability of priority ranking and of the optimal portfolio. Besides, it is possible to deal with other constraints than the budget limitation, such as to force the inclusion or exclusion of projects from the portfolio or to model the mutually exclusion between projects.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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