Comparison of Benchmarking Methods with and without a Survey Error Model
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
Summary For a target socio‐economic variable, two sources of data with different precisions and collecting frequencies may be available. Typically, the less frequent data (e.g., annual report or census) are more reliable and are considered as benchmarks. The process of using them to adjust the more frequent and less reliable data (e.g., repeated monthly surveys) is called benchmarking. In this paper, we show the relationship among three types of benchmarking methods in the literature, namely the Denton (original and modified), the regression, and the signal‐extraction methods. A new method called “quasi‐linear regression” is proposed under the multiplicative assumption. The numerical Denton method is currently widely used. The aim of this paper is to promote the other two methods which are statistically model‐based; the model for the survey error is assumed to be known. Assuming the survey‐error series follows an autoregressive model of order 1, by simulation, we investigate the impact of misspecification of the model on the benchmarking prediction according to the criterion of minimizing the root‐mean‐squared error of prediction. It is concluded that both statistical methods have great advantages over the Denton method and they are robust to misspecification of the survey‐error model. The problem of how to obtain a survey‐error model is also mentioned.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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