Bibliographic record
Abstract
Abstract The interrelationships between two sets of measurements made on the same subjects can be studied by canonical correlation. The canonical correlation is the maximum correlation between linear functions or canonical factors of two sets of variables. An alternative pair of statistics to investigate the interrelationships between two sets of variables are the redundancy indices. A redundancy index is an indication of the average proportion of variance in the variables in one set that is reproducible from the variables in the other set. Unlike canonical correlation, redundancy indices are non‐symmetric in that a measure can be calculated for each set of variables (predictor and criterion) and need not be equal to each other. A method of extracting factors that maximize redundancy, as opposed to canonical correlation, has been developed as well as various extensions of this methodology. More recently, extended redundancy analysis has been developed to generalize redundancy analysis to investigate asymmetric or directional associations among more than two sets of variables, analogous to generalized canonical correlation analysis. A sports marketing application is provided examining the relationship between the different ways consumers/fans follow their college football team and their various attitudes, opinions, and lifestyles (i.e., psychographics ) regarding sports.
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.
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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".