Coding Voter Turnout Responses in the Current Population Survey
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
Journal Article Coding Voter Turnout Responses in the Current Population Survey Get access Aram Hur, Aram Hur Aram Hur is a PhD candidate in the Politics Department at Princeton University, Princeton, NJ, USA. Christopher H. Achen is a professor in the Politics Department at Princeton University, Princeton, NJ, USA. An earlier version of this research note was presented at the McGill-Princeton Workshop on the Overreporting of Voter Turnout, November 4–5, 2010, at McGill University, Montreal, Quebec, Canada, and May 6, 2011, at Princeton University. The authors are grateful for financial assistance from McGill's Centre for the Study of Democratic Citizenship, under the direction of Elisabeth Gidengil, and from Princeton's Canadian Studies Program. Princeton's Center for the Study of Democratic Politics also provided financial assistance and administrative support. The authors thank all the participants for many helpful comments and suggestions, especially Kurt Bauman, André Blais, Tiffany Julian, and Michael McDonald. Larry Bartels, Simon Jackman, Lynn Vavreck, and anonymous reviewers also made important suggestions and helped dramatically improve the graphical presentations. Remaining errors are the authors' own. Search for other works by this author on: Oxford Academic Google Scholar Christopher H. Achen Christopher H. Achen * Aram Hur is a PhD candidate in the Politics Department at Princeton University, Princeton, NJ, USA. Christopher H. Achen is a professor in the Politics Department at Princeton University, Princeton, NJ, USA. An earlier version of this research note was presented at the McGill-Princeton Workshop on the Overreporting of Voter Turnout, November 4–5, 2010, at McGill University, Montreal, Quebec, Canada, and May 6, 2011, at Princeton University. The authors are grateful for financial assistance from McGill's Centre for the Study of Democratic Citizenship, under the direction of Elisabeth Gidengil, and from Princeton's Canadian Studies Program. Princeton's Center for the Study of Democratic Politics also provided financial assistance and administrative support. The authors thank all the participants for many helpful comments and suggestions, especially Kurt Bauman, André Blais, Tiffany Julian, and Michael McDonald. Larry Bartels, Simon Jackman, Lynn Vavreck, and anonymous reviewers also made important suggestions and helped dramatically improve the graphical presentations. Remaining errors are the authors' own. *Address correspondence to Christopher H. Achen, Politics Department, Princeton University, 312 Robertson Hall, Princeton, NJ 08544, USA; e-mail: achen@princeton.edu. Search for other works by this author on: Oxford Academic Google Scholar Public Opinion Quarterly, Volume 77, Issue 4, Winter 2013, Pages 985–993, https://doi.org/10.1093/poq/nft042 Published: 25 November 2013
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it