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
Pairwise comparison matrices form the basis of the AHP.We provide optimal sets of comparisons of incomplete pairwise comparison matrices (IPCMs) in case of given number of alternatives and given number of comparisons, i.e., the ones that provide the closest weight vectors to the vectors calculated from the complete case.We conduct extensive simulations to compare the different structures of comparisons.It turns out that the regularity and bipartiteness of the representing graphs of IPCMs are the most important properties connected to optimality.Among spanning trees, the star graph is found to be optimal.In many cases the different optimal sets are reachable from each other by adding exactly one comparison to the previous set, resulting in an optimal sequence of pairwise comparisons.Our findings can be utilized not only by the application of a given optimal set of comparisons but also using an optimal sequence in cases when the number of comparisons given by the decision maker is not guaranteed (e.g., in online questionnaires).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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