Crossing Language Barriers: Using Crossed Random Effects Modelling in Psycholinguistics Research
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 purpose of this paper is to provide a brief review of multilevel modelling (MLM), also called hierarchical linear modelling (HLM), and to present a step-by-step tutorial on how to perform a crossed random effects model (CREM) analysis. The first part provides an overview of how hierarchical data have been analyzed in the past and how they are being analyzed presently. It then focuses on how these types of data have been dealt with in psycholinguistic research. It concludes with an overview of the steps involved in CREM, a form of MLM used for psycholinguistics data. The second part includes a tutorial demonstrating how to conduct a CREM analysis in SPSS, using the following steps: 1) clarify your research question, 2) determine if CREM is necessary, 3) choose an estimation method, 4) build your model, and 5) estimate the model's effect size. A short example on how to report CREM results in a scholarly article is also included.
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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 | 0.027 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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