Maximum Likelihood Item Response Theory Estimation
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
Abstract An overview and short didactic for three common estimation techniques for parameters in item response theory models are described: (a) joint maximum likelihood, (b) conditional maximum likelihood, and (c) marginal maximum likelihood. Specifically, likelihood equations fundamental to all three estimation approaches are presented first and joint as well as conditional maximum likelihood estimation are briefly described next. The main part of the entry focuses on an extended description of marginal maximum likelihood estimation and illustrates, in a step‐by‐step fashion, how it makes use of the EM algorithm and a fully Bayesian estimation framework to estimate item parameters. A brief description of how person parameters are subsequently estimated follows. Finally, a few words on alternative estimation approaches are offered.
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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 | 0.012 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it