The Jackknife and Multilevel Modeling: A New Application of an Old Trick
Bibliographic record
Abstract
In this article the authors demonstrate two instances where the jackknife can be used to enhance hierarchical linear model (HLM) analyses. The jackknife was used to improve the HLM estimates of composite measures by jackknifing over items. The first study examined fixed-effects and variance component estimation. The jackknife appeared to reduce the bias in the estimates both of slopes and of variances by implicitly adjusting for item-by-person and item-by-group interactions. The second study examined the utility of the jackknife as a multilevel item analysis tool. The results suggest that pseudovalues offer a unique opportunity for isolating item variability in multilevel data. The jackknife seems to offer enhancements and insights to conventional HLM analyses.
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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 | 0.082 | 0.219 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".