Non-Traditional Correlation Analysis: Explanatory Power and Opportunities for Knowledge Discovery in Democracy Studies
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
The possibility of successful applications of the modified correlation coefficient is demonstrated. The latter was proposed by Lukashin nearly twenty five years ago and has been unused since then. A multivariate generalization of this coefficient is proposed. The modified correlation coefficients provide an efficient tool to develop a new multivariate classification method, i.e. a technique for grouping of objects that occurs together with their ranking. As an example of application of the new method, the data of Freedom House is used. NCA (Non-traditional Correlation Analysis), along with similar unconventional methods as FCA (Formal Concept Analysis) and QCA (Qualitative Comparative Analysis) allow to gain additional knowledge from existing databases and numerous ratings which are produced by different agencies. The latters often lack time and opportunities to deeply analyze them, even to go beyond a simple “averaging”. NCA may give additional opportunities for social researchers to understand social phenomena in its complexity, for in-depth analysis and interpretation of structure of data, to build “hierarchical typologies”, and broadly, for data mining and additional knowledge discovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it