DEVELOPMENT OF A ‘BIPOLAR’ R‐INDEX<sup>1</sup>
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
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 (R more or R less ) 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.
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) | 0.001 | 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) | 0.001 | 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