Supplementary Appendix to \Sequential Estimation of Structural Models with a Fixed Point Constraint"
Bibliographic record
Abstract
This supplementary appendix contains the following details omitted from the main paper due to space constraints: (A) numerical implementation of the sequential algorithm based on the RPM, (B) the sequential GMM estimator, (C) the convergence properties of the NPL algorithm for models with unobserved heterogeneity, (D) relative efficiency of the NPL, q-NPL, and MLE, and (E) the equivalence of the NPL estimator using Λ(P, θ) and the NPL estimator using Ψ(P, θ). A Numerical Implementation of the Sequential Algorithm based on the RPM in Section 4.2 Implementing the sequential algorithm based on the RPM in Section 4.2 requires evaluating (I − Π ( ˜ θj−1, ˜ Pj−1)∇P ′Ψ( ˜ θj−1, ˜ Pj−1)Π ( ˜ θj−1, ˜ Pj−1)) −1 as well as computing an orthonormal basis Z ( ˜ θj−1, ˜ Pj−1) from the eigenvectors of ∇P ′Ψ( ˜ θj−1, ˜ Pj−1) for j = 1,..., k. This is potentially costly when the analytical expression of ∇P ′Ψ(θ, P) is not available. In this section, we discuss how to reduce the computational cost of implementing the RPM algorithm by updating (I − Π ( ˜ θj−1, ˜ Pj−1)∇P ′Ψ( ˜ θj−1, ˜ Pj−1)Π ( ˜ θj−1, ˜ Pj−1)) −1 and Z ( ˜ θj−1, ˜ Pj−1) without explicitly computing ∇P ′Ψ(θ, P) in each iteration. Denote ˜ Πj−1 = Π ( ˜ θj−1, ˜ Pj−1),
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.679 | 0.207 |
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".