Uncovering and Displaying the Coherent Groups of Rank Data by\n Exploratory Riffle Shuffling
Bibliographic record
Abstract
Let n respondents rank order d items, and suppose that d << n. Our main task\nis to uncover and display the structure of the observed rank data by an\nexploratory riffle shuffling procedure which sequentially decomposes the n\nvoters into a finite number of coherent groups plus a noisy group : where the\nnoisy group represents the outlier voters and each coherent group is composed\nof a finite number of coherent clusters. We consider exploratory riffle\nshuffling of a set of items to be equivalent to optimal two blocks seriation of\nthe items with crossing of some scores between the two blocks. A riffle\nshuffled coherent cluster of voters within its coherent group is essentially\ncharacterized by the following facts : a) Voters have identical first TCA\nfactor score, where TCA designates taxicab correspondence analysis, an L1\nvariant of correspondence analysis ; b) Any preference is easily interpreted as\nriffle shuffling of its items ; c) The nature of different riffle shuffling of\nitems can be seen in the structure of the contingency table of the first-order\nmarginals constructed from the Borda scorings of the voters ; d) The first TCA\nfactor scores of the items of a coherent cluster are interpreted as Borda scale\nof the items. We also introduce a crossing index, which measures the extent of\ncrossing of scores of voters between the two blocks seriation of the items. The\nnovel approach is explained on the benchmarking SUSHI data set, where we show\nthat this data set has a very simple structure, which can also be communicated\nin a tabular form.\n
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".