Un indice général d’association entre deux variables continues; A general non-linear index of association for two continuous variables
Bibliographic record
Abstract
Measuring and assessing the degree of association between two continuous variables, say Xand Y, has heretofore been restricted by the mandatory specification of a parametric model, be it linear (simple or polynomial), cyclic, autoregressive, or other. We propose as a new quantifying principle the idea that, if a variable Yis in some way linked to a variable X, values of Yimmediately neighbouring on Xshould differ less than non-neighbouring ones, so that the permutative variance (i.e. variance of successive differences) of the Yconcomitants of Xshould be low. Two indices, one asymmetrical (Yon X), the other symmetrical (Ycum X), are explored and exemplified, and their appropriate critical values,power characteristics and relative merits are established.
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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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".