Constituency Preferences and Assignment to Agriculture Committees
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
With only a handful of exceptions, most research examining the impact of constituency preferences on committee assignments in legislatures outside the USA is comprised of single-case studies. This raises the question whether the impact of constituency preferences on committee assignments seen in previous studies apply cross-nationally. Focusing on committees whose remit includes agricultural affairs—because such committees may be particularly sensitive to constituents’ particularistic interests—this study examines the impact of constituency preferences on committee assignments in 29 legislatures. The analysis suggests that constituency preferences may impact committee assignments in legislatures around the world, and that this effect varies to only a limited extent according to differences in electoral systems, committee organisation and the partisan consequences of personal vote-seeking.
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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 | 0.000 | 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 | 0.000 |
| 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