An Associational Examination of the CaptialCube Effect Context for the MPV over the Linguistic Partitions: Testing Sensitivity & Specificity
Bibliographic record
Abstract
In this third examination of the CapitalCubeÔ Market Navigation Platform [CCMNP] we have selected the previously vetted set of embedded variables: Market Performance Variables [MPV] for their Linguistic Qualifiers [LQ] considering their directional market effects or MPV[LQ[{Neutral: Unfavorable: Favorable}]]. In the testing, we are interested in the Sensitivity and the Specificity of these vetted variables over the annual S&P500 Panel from 2005 to 2013. The inference framework employed a Median Split: High or Low for each of the 13 MPV tested and a random selection to avoid the FPE-jeopardy that is part of the Chi2 testing model. We used the Tamhane & Dunlop cut-off to identify Chi2 cells effects of interest and used these to develop the Sensitivity and the Specificity tests. Results: We were able to reject the a priori Nulls proffered for the testing protocols indicating that one may reject the supposition that the labeling of the LQ is formed by random processes in the CCMNP.
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How this classification was reachedexpand
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".