DEVELOPMENT OF A ‘BIPOLAR’ R‐INDEX<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT A new ‘bipolar’ R‐index analysis was proposed and evaluated. Eighteen judges evaluated red color in eight wine samples by comparing each sample with the control. Judges indicated whether the sample had ‘more’, the 'same’, or ‘less’ red color than the control, and whether they were sure or unsure of their decision. Three computational methods were used to examine the results: the ‘traditional’ R‐index, the ‘bipolar’ R‐index (Rmore or Rless) and the ‘weighted‐bipolar’ R‐index. While all three methods provided consistent results, the ‘bipolar’ R‐indices reflected bidirectional differences among the samples thus providing more information. A refinement to the computation (‘weighted‐bipolar’ R‐index) was an approach for eliminating the bias associated with overestimation of the sample size and accordingly changed some of the significance levels. Further research is currently underway to expand the scope and application of this method.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".