Bibliographic record
Abstract
there has been a decrease in the extent to which members transfer after their third term. A more routinized assignment process has important implications for the level of participation and sense of achievement experienced by individual members, for the influence House leaders are able to exert, and for the integrity and representativeness of the House as an institution. The standing committees of Congress have counted for a great deal since at least the late 19th century (Wilson, 1885), and they are likely to continue to serve as preeminent decisionmaking agencies in Congress during the last quarter of the 20th century, as well. Whatever their future, few would deny their contemporary importance in providing a forum for the discussion of proposed legislation, in shaping the content and limits of these measures, and in providing individual lawmakers with opportunities to realize their vocational, political, and legislative goals. Among the many paths to influence in the House of Representatives particularly, virtually all require (1) assignment to a committee which is important to a member; (2) development of an expertise and reputation grounded in one's performance on a committee; (3) manifest expression of a member's priorities and values in the legislation reported out by the committee; and (4) engineering a majority for the version of a legislative measure the member has come to prefer. But first there must be assignment to a desirable committee, and the recent increase in research devoted to the committee assignment process in
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".