Comparison of Benchmarking Methods with and without a Survey Error Model
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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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 | 0.045 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".