Bibliographic record
Abstract
Abstract Legal scholars and social scientists have long traced how private attorneys influence judicial behaviour. By contrast, a cohesive and comparative agenda probing how government lawyers impact the courts remains elusive. This chapter serves as a springboard for this agenda by identifying three ways that government attorneys influence judicial behaviour: by shaping judicial agendas, decisions, and autonomy. The chapter suggests that each mode of influence tends to be wielded by a distinct type of government lawyer—public prosecutors, government litigators, and executive branch attorneys—and illustrates the mechanisms driving their influence over judges via concrete examples. First, the author spotlights research delineating how government lawyers in law enforcement roles can wield a ‘politics of discretion’ to shape judges’ agendas and their supervisory capacity by strategically withholding or prioritizing particular lawsuits. Next, he highlights studies demonstrating how attorneys representing governments in court can engage in a ‘politics of positionality’, leveraging their role as intermediaries and repeat players to influence judgments—provided that their credibility as litigators is not hampered by overt politicization. Finally, the chapter chronicles a burgeoning literature on attorneys in the executive branch who weaponize their legal training to undermine judicial independence and manufacture obeisance—what the author calls ‘power politics, lawyer-style’.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".