www.elsevier.com/locate/electstud The rolling cross-section design
Bibliographic record
Abstract
This article describes the ‘rolling cross-section’, a design well-adapted to telephone surveys and to capturing real-time effects in campaigns. In one sense, the design is just a standard cross-section, but the day on which a respondent is interviewed is chosen randomly. As a result, analysis of longitudinal factors is possible with only modest controls. The design necessitates an estimation strategy that distinguishes time-series from cross-sectional effects. We outline alternative strategies and show that the design is especially powerful if it is wedded to a post-election panel wave. We also show how graphical analysis enhances its power. Illustrative examples are drawn from the 1993 Canadian Election Study. We compare the design to some obvious alternatives and argue that, for reasons of cost and simplicity, any national election study based on telephone interviewing is best conducted this way. 2002 Elsevier Science Ltd. All rights reserved. The ‘rolling cross-section ’ (RCS) is a design that facilitates detailed exploration of campaign dynamics. Its essence is to take a one-shot cross-section and distribute interviewing in a controlled way over time. Properly done, the date on which a
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How this classification was reachedexpand
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 | 0.069 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".