Signal Cancellation Recovery of Factors
Bibliographic record
Abstract
This poster introduced Signal Cancellation Recovery of Factors (SCRoF), a powerful rotation-free alternate approach to exploratory factor analysis. SCRoF does not involve matrix decomposition but rather proceeds from pair-wise signal cancellation. The relative weight of two variables exclusive to the same factor can always be adjusted for their difference to cancel the signal received from their common factor. The resulting optimal contrast, consisting exclusively of a combination of the unique variances of the two variables, is obtained by minimizing the squared correlations of the contrast with all remaining variables. All necessary factor loadings and factor correlations are derived from the successful signal cancellation loadings within a two-threshold statistical strategy that may produce alternate solutions mutually compatible with the data. The procedure is illustrated on synthetic data for a complex 6-factor structure and then applied to real data used in Tabachnik & Fidell (2019) to illustrate structural equation modelling. While LISREL suggested one cross-loading to make the X^2 fit not-significant, SCRoF documents that two cross-loadings are required. This illustrates that signal cancellation taps information not exploited by structural equation modelling.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".