Bibliographic record
Abstract
Abstract In broad terms, factor analysis focuses on the link between a set of intercorrelated variables and their representation by a smaller set of conceptually meaningful megavariables, termed factors . Within this all‐encompassing umbrella description, factor analysis is more precisely characterized in terms of its function. If used to determine the extent to which the set of variables can be adequately represented by a smaller number of factors, then we are describing exploratory factor analysis . If, on the other hand, it is used in determining the extent to which a set of variables are adequately represented by a smaller number of factors as postulated by theory and/or empirical research, then we are describing confirmatory factor analysis . In providing a more comprehensive explanation of factor analysis, I first address the general notion of factor analysis and then follow with a more extensive description of both exploratory and confirmatory factor analysis, together with a comparative summary of these two factor analytic approaches. Finally, I close this article by addressing important issues and caveats associated with the application of these approaches in psychological research.
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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 | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.025 |
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".